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Someone still needs to decide whether a price should be averaged, whether two terms mean the same thing, and which questions the model should answer. Generating the definition is the easy part; reviewing it is the work.\u003C/p\u003E\n","\u003Cp\u003EIn this guide, you start with public data from Snowflake Marketplace. You create two semantic views, check them against hand-written SQL, refine their vocabulary and instructions, and connect both to a Cortex Agent through Cortex Analyst tools.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003EThis guide uses SQL for every step.\u003C/strong\u003E Snowflake also offers other ways to do most of this work: Semantic View Autopilot and Semantic Studio in Snowsight for semantic views, the Snowsight agent editor for agents, and Cortex Code skills that generate definitions from a prompt. They produce the same objects. This guide mentions them where they apply but doesn't walk through them. SQL definitions are explicit, reviewable in a diff, and easy to put under version control.\u003C/p\u003E\n","\u003Ch3\u003EPrerequisites\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EComfort writing SQL, including joins and aggregates.\u003C/li\u003E\u003Cli\u003EA Snowflake account with access to Cortex Analyst and Cortex Agents.\u003C/li\u003E\u003Cli\u003EAn administrator who can approve Marketplace acquisition and provision the lab privileges if you cannot do so yourself.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Learn\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EScope a semantic view around a persona and a set of questions.\u003C/li\u003E\u003Cli\u003EWrite semantic views with \u003Ccode\u003ECREATE SEMANTIC VIEW\u003C/code\u003E and refine them with \u003Ccode\u003ECREATE OR ALTER\u003C/code\u003E.\u003C/li\u003E\u003Cli\u003EAdd synonyms, SQL generation instructions, and verified queries.\u003C/li\u003E\u003Cli\u003ECheck semantic view results against reference SQL.\u003C/li\u003E\u003Cli\u003ECreate an agent with two Cortex Analyst tools using \u003Ccode\u003ECREATE AGENT\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003EWhat You'll Need\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EA SQL worksheet in Snowsight, or the \u003Ca href=\"https://docs.snowflake.com/en/developer-guide/snowflake-cli/installation/installation\"\u003ESnowflake CLI\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EAccess to \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTSZ290BV255\"\u003ESnowflake Curated Data (Free)\u003C/a\u003E.\u003C/li\u003E\u003Cli\u003EAn existing, approved warehouse for the exercises.\u003C/li\u003E\u003Cli\u003EA lab role with access to the imported data and permission to create a database, or a dedicated database provisioned by an administrator.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe lab role needs \u003Ccode\u003EUSAGE\u003C/code\u003E on the warehouse and the lab database and schema, plus \u003Ccode\u003ECREATE VIEW\u003C/code\u003E, \u003Ccode\u003ECREATE SEMANTIC VIEW\u003C/code\u003E, and \u003Ccode\u003ECREATE AGENT\u003C/code\u003E on the lab schema, and the appropriate Cortex feature access. It also needs read access to the source data. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-setup\"\u003ECortex Agent access control\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EDo not change account-wide AI settings for this exercise without administrator approval. Model availability and any cross-region inference settings must meet your organization's requirements.\u003C/p\u003E\n\u003Cblockquote\u003E\n","\u003Cp\u003EThe listing has no data subscription charge under its free tier, but warehouse queries and Cortex services incur charges. The free listing has a three-month data lag; this guide deliberately uses a historical period.\u003C/p\u003E\n\u003C/blockquote\u003E\n","\u003Ch3\u003EWhat You'll Build\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EThree small SQL views that scope the public source data without copying it.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EEQUITY_MARKET_SV\u003C/code\u003E and \u003Ccode\u003EMORTGAGE_MARKET_SV\u003C/code\u003E, two semantic views written in SQL.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EMARKETS_DESK_AGENT\u003C/code\u003E, with one Cortex Analyst tool per semantic view.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch2\u003EDefine the Questions\u003C/h2\u003E\n","\u003Cp\u003EYour persona is a \u003Cstrong\u003Efinancial news writer\u003C/strong\u003E on a markets desk, looking back at 2024. The writer covers two beats: big tech stocks and borrowing costs. They need accurate historical figures, not investment recommendations or forecasts.\u003C/p\u003E\n","\u003Cp\u003EThe two beats produce different questions, so each gets its own semantic view. This is the scoping rule to take away: keep data in one semantic view when the persona's questions combine it, and split where the questions split.\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ETopic\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EQuestions it answers\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EQuestions it does not answer\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EEquity market\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EObserved stock prices and Nasdaq trading volume for AAPL, MSFT, and AMZN during 2024\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EPortfolio returns, dividends, buy/sell recommendations, mortgage rates\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMortgage market\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EUS national 15-year and 30-year fixed mortgage-rate observations during 2024\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EIndividual loan quotes, regional rates, stock prices\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EUse these questions to guide the build:\u003C/p\u003E\n\u003Col\u003E\u003Cli\u003EWhat was AAPL's average post-market closing price in January 2024?\u003C/li\u003E\u003Cli\u003EWhat was MSFT's total Nasdaq trading volume in January 2024?\u003C/li\u003E\u003Cli\u003EShow the monthly average 30-year fixed mortgage rate for 2024, including its source unit.\u003C/li\u003E\u003Cli\u003ECompare the average 15-year and 30-year fixed mortgage rates in January 2024, keeping each series separate.\u003C/li\u003E\u003Cli\u003EWhat was the average rate in January 2024?\u003C/li\u003E\u003C/ol\u003E\n","\u003Cp\u003EThe fifth question is deliberately ambiguous. The desired behaviour is to ask which mortgage term the user means, rather than invent a default.\u003C/p\u003E\n","\u003Cp\u003ESometimes the writer wants a recap that covers both beats. The agent can answer that by querying each semantic view separately and combining the results in one response. It does not join the two views, so questions that need row-level alignment between them, such as "how did stocks move in the weeks when rates rose", belong in a single semantic view instead.\u003C/p\u003E\n","\u003Cp\u003EA \u003Cstrong\u003Esemantic view\u003C/strong\u003E defines business meaning. \u003Cstrong\u003ECortex Analyst\u003C/strong\u003E uses that meaning to generate SQL from a question. The \u003Cstrong\u003Eagent\u003C/strong\u003E chooses tools and presents their results. These are separate responsibilities.\u003C/p\u003E\n","\u003Ch2\u003EGet Public Data\u003C/h2\u003E\n","\u003Ch3\u003EAcquire the listing\u003C/h3\u003E\n","\u003Cp\u003EYour role needs \u003Ccode\u003ECREATE DATABASE\u003C/code\u003E and \u003Ccode\u003EIMPORT SHARE\u003C/code\u003E, or you can use \u003Ccode\u003EACCOUNTADMIN\u003C/code\u003E. Run the commands as a person user whose profile includes a first name, last name, and email address. You can also get the listing from its \u003Ca href=\"https://app.snowflake.com/marketplace/listing/GZTSZ290BV255\"\u003EMarketplace page\u003C/a\u003E in Snowsight.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003E-- Check that the listing is available in your region (is_ready_for_import = TRUE).\nDESCRIBE AVAILABLE LISTING GZTSZ290BV255;\n\n-- Only if is_ready_for_import is FALSE: request the listing and wait for it.\nCALL SYSTEM$REQUEST_LISTING_AND_WAIT('GZTSZ290BV255');\n\n-- Accept the listing terms, then create the imported database.\nCALL SYSTEM$ACCEPT_LEGAL_TERMS('DATA_EXCHANGE_LISTING', 'GZTSZ290BV255');\nCREATE DATABASE SNOWFLAKE_PUBLIC_DATA_FREE FROM LISTING 'GZTSZ290BV255';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EReview the listing terms before you accept them. If accepting the terms with SQL fails, accept them on the listing page in Snowsight, then run the \u003Ccode\u003ECREATE DATABASE\u003C/code\u003E statement. If your account already has the listing under another database name, replace \u003Ccode\u003ESNOWFLAKE_PUBLIC_DATA_FREE\u003C/code\u003E consistently in the SQL below. See \u003Ca href=\"https://docs.snowflake.com/en/collaboration/consumer-listings-progaccess-examples\"\u003EManage listings with SQL as a consumer\u003C/a\u003E.\u003C/p\u003E\n","\u003Cp\u003EIf another role created the database, grant \u003Ccode\u003EIMPORTED PRIVILEGES\u003C/code\u003E on it to your lab role. Do not create lab objects inside the imported database. It contains provider-managed, read-only data.\u003C/p\u003E\n","\u003Cp\u003EConfirm the schema and object names:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESHOW VIEWS IN SCHEMA SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe source objects used in this guide are:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ccode\u003ESTOCK_PRICE_TIMESERIES\u003C/code\u003E: dated price and trading-volume observations.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EFREDDIE_MAC_HOUSING_TIMESERIES\u003C/code\u003E: dated housing and mortgage observations.\u003C/li\u003E\u003Cli\u003E\u003Ccode\u003EFREDDIE_MAC_HOUSING_ATTRIBUTES\u003C/code\u003E: definitions and attributes for those housing variables.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThe provider documents the \u003Ca href=\"https://data-docs.snowflake.com/sources/stocks\"\u003Estock data\u003C/a\u003E and \u003Ca href=\"https://data-docs.snowflake.com/sources/freddie-mac\"\u003EFreddie Mac data\u003C/a\u003E.\u003C/p\u003E\n","\u003Ch3\u003EPrepare the lab schema\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT CURRENT_ACCOUNT(), CURRENT_ROLE(), CURRENT_WAREHOUSE();\n\nCREATE DATABASE SEMANTIC_VIEWS_LAB;\nCREATE SCHEMA SEMANTIC_VIEWS_LAB.DEMO;\nUSE SCHEMA SEMANTIC_VIEWS_LAB.DEMO;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThese statements intentionally do not replace existing objects. If the database name is already taken, choose a unique lab name and update all subsequent SQL. If an administrator provisioned the database and schema, skip the two \u003Ccode\u003ECREATE\u003C/code\u003E statements but keep the \u003Ccode\u003EUSE SCHEMA\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EScope the source data\u003C/h3\u003E\n","\u003Cp\u003EPublic time-series datasets often store different measures in a shared \u003Ccode\u003EVALUE\u003C/code\u003E column. A price, a volume, and an interest rate are not interchangeable just because they are numeric.\u003C/p\u003E\n","\u003Cp\u003ECreate three small SQL views to keep the exercise focused. These views filter and select columns; they do not copy the data.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE VIEW SEMANTIC_VIEWS_LAB.DEMO.EQUITY_OBSERVATIONS AS\nSELECT\n    TICKER,\n    DATE AS OBSERVATION_DATE,\n    VARIABLE_NAME,\n    VALUE\nFROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.STOCK_PRICE_TIMESERIES\nWHERE TICKER IN ('AAPL', 'MSFT', 'AMZN')\n  AND VARIABLE_NAME IN ('Post-Market Close', 'Nasdaq Volume')\n  AND DATE >= '2024-01-01'::DATE\n  AND DATE < '2025-01-01'::DATE;\n\nCREATE VIEW SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_OBSERVATIONS AS\nSELECT\n    GEO_ID,\n    VARIABLE,\n    DATE AS OBSERVATION_DATE,\n    VALUE,\n    UNIT\nFROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.FREDDIE_MAC_HOUSING_TIMESERIES\nWHERE GEO_ID = 'country/USA'\n  AND DATE >= '2024-01-01'::DATE\n  AND DATE < '2025-01-01'::DATE\n  AND VARIABLE IN (\n      SELECT VARIABLE\n      FROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.FREDDIE_MAC_HOUSING_ATTRIBUTES\n      WHERE VARIABLE_NAME IN (\n          '15-Year Fixed Rate Mortgage Rate, National Average',\n          '30-Year Fixed Rate Mortgage Rate, National Average'\n      )\n  );\n\nCREATE VIEW SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_VARIABLES AS\nSELECT VARIABLE, VARIABLE_NAME, MEASURE, FREQUENCY\nFROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.FREDDIE_MAC_HOUSING_ATTRIBUTES\nWHERE VARIABLE_NAME IN (\n    '15-Year Fixed Rate Mortgage Rate, National Average',\n    '30-Year Fixed Rate Mortgage Rate, National Average'\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EPreview the selected data:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT TICKER, VARIABLE_NAME, OBSERVATION_DATE, VALUE\nFROM SEMANTIC_VIEWS_LAB.DEMO.EQUITY_OBSERVATIONS\nORDER BY TICKER, VARIABLE_NAME, OBSERVATION_DATE\nLIMIT 12;\n\nSELECT\n    attributes.VARIABLE_NAME,\n    observations.OBSERVATION_DATE,\n    observations.VALUE,\n    observations.UNIT,\n    attributes.FREQUENCY\nFROM SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_OBSERVATIONS AS observations\nJOIN SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_VARIABLES AS attributes\n    ON observations.VARIABLE = attributes.VARIABLE\nORDER BY attributes.VARIABLE_NAME, observations.OBSERVATION_DATE\nLIMIT 12;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECheckpoint:\u003C/strong\u003E Both queries return data. Look closely at the mortgage rows: the \u003Ccode\u003EUNIT\u003C/code\u003E column says \u003Ccode\u003EPercent\u003C/code\u003E, but the values are fractions, such as \u003Ccode\u003E0.066\u003C/code\u003E for a 6.6% rate. The guide keeps the source values as they are and makes sure every answer reports them with their unit. Don't rescale them based on magnitude alone. The mortgage series is weekly, so a monthly average is an average of about four observations.\u003C/p\u003E\n","\u003Ch2\u003ECreate the Equity View\u003C/h2\u003E\n","\u003Cp\u003EStart with a single logical table. Relationships come in the second view.\u003C/p\u003E\n","\u003Ch3\u003EWrite the definition\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE SEMANTIC VIEW SEMANTIC_VIEWS_LAB.DEMO.EQUITY_MARKET_SV\n  TABLES (\n    equity AS SEMANTIC_VIEWS_LAB.DEMO.EQUITY_OBSERVATIONS\n      COMMENT = 'Daily equity observations for AAPL, MSFT, and AMZN in 2024. One row per ticker, date, and series.'\n  )\n  FACTS (\n    equity.value AS VALUE\n      COMMENT = 'Observed value. Its meaning depends on variable_name: a price for Post-Market Close, a share count for Nasdaq Volume.'\n  )\n  DIMENSIONS (\n    equity.ticker AS TICKER\n      WITH SYNONYMS = ('stock', 'symbol')\n      COMMENT = 'Stock ticker symbol: AAPL, MSFT, or AMZN.',\n    equity.observation_date AS OBSERVATION_DATE\n      COMMENT = 'Trading date of the observation.',\n    equity.variable_name AS VARIABLE_NAME\n      COMMENT = 'Series name: Post-Market Close or Nasdaq Volume.'\n  )\n  METRICS (\n    equity.avg_post_market_close AS AVG(CASE WHEN VARIABLE_NAME = 'Post-Market Close' THEN VALUE END)\n      COMMENT = 'Arithmetic mean of non-null Post-Market Close observations, in the source price unit.',\n    equity.total_nasdaq_volume AS SUM(CASE WHEN VARIABLE_NAME = 'Nasdaq Volume' THEN VALUE END)\n      COMMENT = 'Sum of non-null Nasdaq Volume observations (shares traded).'\n  )\n  COMMENT = 'Historical equity prices and Nasdaq volume for AAPL, MSFT, and AMZN in 2024. No dividends, returns, forecasts, or mortgage rates.';\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe building blocks are:\u003C/p\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EClause\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003EPurpose in this example\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ETABLES\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMaps the logical table \u003Ccode\u003Eequity\u003C/code\u003E to the scoped SQL view\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EFACTS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERow-level values that metrics are built from\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EDIMENSIONS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ETicker, trading date, and the source series name\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003EMETRICS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EAggregations, each limited to one series\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ECOMMENT\u003C/code\u003E and \u003Ccode\u003EWITH SYNONYMS\u003C/code\u003E\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EBusiness meaning and approved alternative terms; Cortex Analyst reads them\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Cp\u003EThe conditional \u003Ccode\u003ECASE\u003C/code\u003E expressions are the important design decision. The source stores prices and volumes in the same \u003Ccode\u003EVALUE\u003C/code\u003E column, so a plain \u003Ccode\u003EAVG(VALUE)\u003C/code\u003E would average prices with share counts. Each metric includes only its own series.\u003C/p\u003E\n","\u003Cp\u003ESemantic View Autopilot can generate a first draft like this from a table and a business description, and a Cortex Code skill can write one from a prompt. Either way, review the definition against the points above before you rely on it.\u003C/p\u003E\n","\u003Ch3\u003ECheck it against reference SQL\u003C/h3\u003E\n","\u003Cp\u003EA semantic view that deploys is syntactically valid. That doesn't make its calculations right. Compare a metric with SQL you wrote by hand:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT 'reference' AS source, AVG(VALUE) AS avg_post_market_close\nFROM SEMANTIC_VIEWS_LAB.DEMO.EQUITY_OBSERVATIONS\nWHERE VARIABLE_NAME = 'Post-Market Close'\n  AND TICKER = 'AAPL'\n  AND OBSERVATION_DATE >= '2024-01-01'::DATE\n  AND OBSERVATION_DATE < '2024-02-01'::DATE\nUNION ALL\nSELECT 'semantic view', avg_post_market_close\nFROM SEMANTIC_VIEW(\n    SEMANTIC_VIEWS_LAB.DEMO.EQUITY_MARKET_SV\n    DIMENSIONS equity.ticker\n    METRICS equity.avg_post_market_close\n    WHERE equity.ticker = 'AAPL'\n      AND equity.observation_date >= '2024-01-01'::DATE\n      AND equity.observation_date < '2024-02-01'::DATE\n);\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECheckpoint:\u003C/strong\u003E Both rows show the same value. If they differ, inspect the metric expression and filters before continuing. Record the value as your reference answer for this run of the lab; historical data can be revised, so the guide doesn't hard-code it.\u003C/p\u003E\n","\u003Ch2\u003ERefine the Equity View\u003C/h2\u003E\n","\u003Cp\u003E\u003Ccode\u003ECREATE OR ALTER SEMANTIC VIEW\u003C/code\u003E changes an existing semantic view to match the statement. Any property you leave out is removed, so always submit the complete definition.\u003C/p\u003E\n","\u003Ch3\u003EAdd vocabulary and a verified query\u003C/h3\u003E\n","\u003Cp\u003EFor this exercise, the desk uses \u003Cstrong\u003Eextended-session close\u003C/strong\u003E to mean the provider's \u003Cstrong\u003EPost-Market Close\u003C/strong\u003E series. This is a lab vocabulary decision, not a universal industry definition, so it goes into the metric as a synonym and its comment explains exactly what it means.\u003C/p\u003E\n","\u003Cp\u003EYou also add a \u003Cstrong\u003Everified query\u003C/strong\u003E: a question paired with SQL that you have checked. Cortex Analyst uses verified queries as examples when generating SQL, and when a question matches one exactly, the agent runs its SQL directly. In verified-query SQL, refer to a logical table by its name with a double-underscore prefix, such as \u003Ccode\u003E__equity\u003C/code\u003E.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR ALTER SEMANTIC VIEW SEMANTIC_VIEWS_LAB.DEMO.EQUITY_MARKET_SV\n  TABLES (\n    equity AS SEMANTIC_VIEWS_LAB.DEMO.EQUITY_OBSERVATIONS\n      COMMENT = 'Daily equity observations for AAPL, MSFT, and AMZN in 2024. One row per ticker, date, and series.'\n  )\n  FACTS (\n    equity.value AS VALUE\n      COMMENT = 'Observed value. Its meaning depends on variable_name: a price for Post-Market Close, a share count for Nasdaq Volume.'\n  )\n  DIMENSIONS (\n    equity.ticker AS TICKER\n      WITH SYNONYMS = ('stock', 'symbol')\n      COMMENT = 'Stock ticker symbol: AAPL, MSFT, or AMZN.',\n    equity.observation_date AS OBSERVATION_DATE\n      COMMENT = 'Trading date of the observation.',\n    equity.variable_name AS VARIABLE_NAME\n      COMMENT = 'Series name: Post-Market Close or Nasdaq Volume.'\n  )\n  METRICS (\n    equity.avg_post_market_close AS AVG(CASE WHEN VARIABLE_NAME = 'Post-Market Close' THEN VALUE END)\n      WITH SYNONYMS = ('extended-session close')\n      COMMENT = 'Arithmetic mean of non-null Post-Market Close observations, in the source price unit. In this lab, extended-session close means the provider Post-Market Close series. Not the regular-session close, adjusted close, or a return.',\n    equity.total_nasdaq_volume AS SUM(CASE WHEN VARIABLE_NAME = 'Nasdaq Volume' THEN VALUE END)\n      COMMENT = 'Sum of non-null Nasdaq Volume observations (shares traded on Nasdaq, not all-market volume).'\n  )\n  COMMENT = 'Historical equity prices and Nasdaq volume for AAPL, MSFT, and AMZN in 2024. No dividends, returns, forecasts, or mortgage rates.'\n  AI_VERIFIED_QUERIES (\n    aapl_avg_close_jan_2024 AS (\n      QUESTION 'What was AAPL''s average post-market closing price in January 2024?'\n      SQL 'SELECT ticker, AVG(CASE WHEN variable_name = ''Post-Market Close'' THEN value END) AS avg_post_market_close FROM __equity WHERE ticker = ''AAPL'' AND observation_date >= ''2024-01-01'' AND observation_date < ''2024-02-01'' GROUP BY ticker'\n    )\n  );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EA synonym changes vocabulary, not the formula. If two terms mean different calculations, define separate metrics instead. Add synonyms only for terms your users actually use; a long generated list makes matching less precise, not more.\u003C/p\u003E\n","\u003Ch3\u003EReview the stored definition\u003C/h3\u003E\n","\u003Cp\u003ERead the definition back as YAML to confirm what Snowflake stored:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT SYSTEM$READ_YAML_FROM_SEMANTIC_VIEW(\n    'SEMANTIC_VIEWS_LAB.DEMO.EQUITY_MARKET_SV'\n) AS SEMANTIC_VIEW_YAML;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003ECheck that the synonym, the updated comments, and the verified query are present. The YAML form is useful for reviews and for tools that work with YAML; Semantic Studio in Snowsight shows the same definition visually. This guide keeps the SQL statement as the source of truth.\u003C/p\u003E\n","\u003Ch3\u003EKeep filters explicit\u003C/h3\u003E\n","\u003Cp\u003EThe source SQL views enforce the lab's ticker, geography, and 2024 boundaries. The conditional metric expressions enforce which series contributes to each metric. Question-specific date filters further narrow the scope.\u003C/p\u003E\n","\u003Cp\u003EDo not assume that a comment or an instruction restricts every query. Comments and instructions guide Cortex Analyst; they are not constraints or security controls. Inspect generated SQL to confirm that the required predicates are present.\u003C/p\u003E\n","\u003Ch2\u003EAdd the Mortgage View\u003C/h2\u003E\n","\u003Cp\u003EThe second topic is mortgage rates. Here the observations join to series descriptions, which gives you a relationship to review.\u003C/p\u003E\n","\u003Ch3\u003ECheck the join key\u003C/h3\u003E\n","\u003Cp\u003EA relationship assumes that the referenced key is unique. Check it before declaring it. This query must return no rows:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT VARIABLE, COUNT(*) AS ROW_COUNT\nFROM SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_VARIABLES\nGROUP BY VARIABLE\nHAVING VARIABLE IS NULL OR COUNT(*) > 1;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EIf it returns rows, joining would duplicate observations. Fix the source view rather than hiding duplicates with \u003Ccode\u003EDISTINCT\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch3\u003EWrite the definition\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE SEMANTIC VIEW SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_MARKET_SV\n  TABLES (\n    observations AS SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_OBSERVATIONS\n      COMMENT = 'Weekly US national fixed mortgage-rate observations in 2024.',\n    variables AS SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_VARIABLES\n      PRIMARY KEY (VARIABLE)\n      COMMENT = 'One row per mortgage series: 15-year and 30-year fixed.'\n  )\n  RELATIONSHIPS (\n    observations_to_variables AS observations (VARIABLE) REFERENCES variables\n  )\n  FACTS (\n    observations.value AS VALUE\n      COMMENT = 'Observed mortgage rate in the source unit.'\n  )\n  DIMENSIONS (\n    observations.observation_date AS OBSERVATION_DATE\n      COMMENT = 'Date of the weekly observation.',\n    observations.geo_id AS GEO_ID\n      COMMENT = 'Geography. Always country/USA in this lab.',\n    observations.unit AS UNIT\n      COMMENT = 'Source unit of the rate. Report it with every value.',\n    variables.variable_name AS VARIABLE_NAME\n      WITH SYNONYMS = ('mortgage term', 'mortgage series')\n      COMMENT = 'Mortgage series: 15-Year or 30-Year Fixed Rate Mortgage Rate, National Average.',\n    variables.measure AS MEASURE\n      COMMENT = 'Measure described by the series.',\n    variables.frequency AS FREQUENCY\n      COMMENT = 'Observation frequency of the series.'\n  )\n  METRICS (\n    observations.avg_observed_rate AS AVG(VALUE)\n      COMMENT = 'Arithmetic mean of available observations. Always group by variable_name and unit; never combine the 15-year and 30-year series.'\n  )\n  COMMENT = 'US national 15-year and 30-year fixed mortgage rates in 2024. Not loan quotes, APRs, regional rates, or forecasts.'\n  AI_SQL_GENERATION 'If a question asks about a rate without naming the 15-year or 30-year series, ask which one the user means. Never average the two series together. Preserve the source unit.'\n  AI_VERIFIED_QUERIES (\n    compare_15_30_jan_2024 AS (\n      QUESTION 'Compare the average 15-year and 30-year fixed mortgage rates in January 2024.'\n      SQL 'SELECT v.variable_name, o.unit, AVG(o.value) AS avg_observed_rate FROM __observations AS o JOIN __variables AS v ON o.variable = v.variable WHERE o.observation_date >= ''2024-01-01'' AND o.observation_date < ''2024-02-01'' GROUP BY v.variable_name, o.unit ORDER BY v.variable_name, o.unit'\n    )\n  );\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThree things to review:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EThe relationship\u003C/strong\u003E joins observations to variables on the \u003Ccode\u003EVARIABLE\u003C/code\u003E key, not on the human-readable name. Each observation matches exactly one series row, which the key check confirmed.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003EThe metric\u003C/strong\u003E is a plain \u003Ccode\u003EAVG(VALUE)\u003C/code\u003E. It is only meaningful per series and unit, which is why the comment says so.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003EAI_SQL_GENERATION\u003C/code\u003E\u003C/strong\u003E gives Cortex Analyst instructions that apply to every question on this view. Here it handles the ambiguous "rate" question and protects against mixing the two series.\u003C/li\u003E\u003C/ul\u003E\n","\u003Ch3\u003ECheck the second view\u003C/h3\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ESELECT\n    attributes.VARIABLE_NAME,\n    observations.UNIT,\n    AVG(observations.VALUE) AS AVG_OBSERVED_RATE\nFROM SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_OBSERVATIONS AS observations\nJOIN SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_VARIABLES AS attributes\n    ON observations.VARIABLE = attributes.VARIABLE\nWHERE observations.OBSERVATION_DATE >= '2024-01-01'::DATE\n  AND observations.OBSERVATION_DATE < '2024-02-01'::DATE\nGROUP BY attributes.VARIABLE_NAME, observations.UNIT\nORDER BY attributes.VARIABLE_NAME, observations.UNIT;\n\nSELECT * FROM SEMANTIC_VIEW(\n    SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_MARKET_SV\n    DIMENSIONS variables.variable_name, observations.unit\n    METRICS observations.avg_observed_rate\n    WHERE observations.observation_date >= '2024-01-01'::DATE\n      AND observations.observation_date < '2024-02-01'::DATE\n)\nORDER BY variable_name, unit;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECheckpoint:\u003C/strong\u003E Both queries return two rows, one per series, with matching values and units.\u003C/p\u003E\n","\u003Ch2\u003ECreate the Agent\u003C/h2\u003E\n","\u003Cp\u003EThe agent receives a question, decides which tool to call, and writes the answer. Each Cortex Analyst tool points to one semantic view. Tool descriptions drive tool choice, so make each one say what the tool covers and what it doesn't.\u003C/p\u003E\n","\u003Cp\u003EReplace \u003Ccode\u003ECOMPUTE_WH\u003C/code\u003E with your approved warehouse. You can also build or edit the same agent in the Snowsight agent editor.\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003ECREATE OR REPLACE AGENT SEMANTIC_VIEWS_LAB.DEMO.MARKETS_DESK_AGENT\n  COMMENT = 'Answers a markets desk''s historical questions using a small 2024 public-data sample. Not an investment adviser, forecasting service, or live market feed.'\n  PROFILE = '{"display_name": "Markets Desk Assistant"}'\n  FROM SPECIFICATION\n  $$\n  models:\n    orchestration: auto\n\n  instructions:\n    orchestration: |\n      Use EQUITY_MARKET_SV for equity prices and Nasdaq trading volume.\n      Use MORTGAGE_MARKET_SV for national fixed mortgage-rate observations.\n      Use the tools for data-backed answers; do not supply numbers from memory.\n\n      Ask for clarification when a rate question does not identify the mortgage\n      term. Keep different mortgage series and source units in separate groups.\n      If a requested calculation, instrument, or date is outside the configured\n      data, explain the limitation instead of substituting another measure.\n\n      If a question covers both stocks and mortgage rates, query each tool\n      separately, then combine the results in one summary. Attribute each figure\n      to its source. Do not infer relationships, correlations, or causes between\n      the two topics.\n    response: |\n      State the measure, period, ticker or mortgage series, and source unit.\n      Distinguish a period average from a value at a particular date.\n      Report mortgage values exactly as returned with their source unit; do not rescale them.\n      Explain missing data without treating it as zero.\n      Keep responses concise and do not give investment recommendations.\n\n  tools:\n    - tool_spec:\n        type: "cortex_analyst_text_to_sql"\n        name: "EQUITY_MARKET_SV"\n        description: >-\n          Use for observed equity prices and Nasdaq trading volume for AAPL, MSFT,\n          and AMZN in 2024. Supports average Post-Market Close, also called\n          extended-session close in this lab, and total Nasdaq volume.\n          Does not answer mortgage-rate, dividend, portfolio-return, or forecast questions.\n    - tool_spec:\n        type: "cortex_analyst_text_to_sql"\n        name: "MORTGAGE_MARKET_SV"\n        description: >-\n          Use for US national 15-year and 30-year fixed mortgage-rate observations\n          in 2024. Supports averages over available observations, grouped separately\n          by mortgage series and source unit. Does not answer equity-price questions,\n          individual loan quotes, regional mortgage rates, or forecasts.\n\n  tool_resources:\n    EQUITY_MARKET_SV:\n      semantic_view: "SEMANTIC_VIEWS_LAB.DEMO.EQUITY_MARKET_SV"\n      execution_environment:\n        type: "warehouse"\n        warehouse: "COMPUTE_WH"\n    MORTGAGE_MARKET_SV:\n      semantic_view: "SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_MARKET_SV"\n      execution_environment:\n        type: "warehouse"\n        warehouse: "COMPUTE_WH"\n  $$;\n\nDESCRIBE AGENT SEMANTIC_VIEWS_LAB.DEMO.MARKETS_DESK_AGENT;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EThe specification has three parts:\u003C/p\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Einstructions.orchestration\u003C/code\u003E\u003C/strong\u003E guides planning: which tool to use, when to ask for clarification, and how to handle questions that span both topics.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Einstructions.response\u003C/code\u003E\u003C/strong\u003E guides how the answer is written.\u003C/li\u003E\u003Cli\u003E\u003Cstrong\u003E\u003Ccode\u003Etools\u003C/code\u003E and \u003Ccode\u003Etool_resources\u003C/code\u003E\u003C/strong\u003E declare each Cortex Analyst tool and connect it to its semantic view and warehouse.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003EThese instructions guide behaviour; they don't replace access controls or testing. See \u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-manage\"\u003ECreate and manage agents\u003C/a\u003E for the full specification format.\u003C/p\u003E\n","\u003Cp\u003E\u003Cstrong\u003ECheckpoint:\u003C/strong\u003E \u003Ccode\u003EDESCRIBE AGENT\u003C/code\u003E shows both tools and their semantic views. You can ask the agent questions in the Snowsight agent playground, or with SQL through \u003Ccode\u003ESNOWFLAKE.CORTEX.DATA_AGENT_RUN\u003C/code\u003E.\u003C/p\u003E\n","\u003Ch2\u003ETroubleshoot and Clean Up\u003C/h2\u003E\n","\u003Ch3\u003ETroubleshooting\u003C/h3\u003E\n\u003Ctable\u003E\u003Cthead\u003E\u003Ctr\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ESymptom\u003C/th\u003E\u003Cth colspan=\"1\" rowspan=\"1\"\u003ECheck\u003C/th\u003E\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe listing cannot be acquired\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003ERole privileges, user profile details, listing availability, and terms\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA source query returns no rows\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EImported database and schema names, series names, and historical coverage\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003E\u003Ccode\u003ECREATE SEMANTIC VIEW\u003C/code\u003E fails\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EClause order (\u003Ccode\u003EFACTS\u003C/code\u003E before \u003Ccode\u003EDIMENSIONS\u003C/code\u003E), logical table names, and doubled single quotes inside string literals\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EA metric looks inflated\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMixed series, duplicate keys in a referenced table, or an incorrect relationship\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMortgage rates look 100 times too small\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe source stores fractions with unit \u003Ccode\u003EPercent\u003C/code\u003E; report values as returned with their unit\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe agent selects the wrong tool\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EMake tool descriptions more distinct and retest\u003C/td\u003E\u003C/tr\u003E\u003Ctr\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe agent reports an inaccessible tool\u003C/td\u003E\u003Ctd colspan=\"1\" rowspan=\"1\"\u003EThe caller's role, warehouse access, semantic view access, and underlying data privileges\u003C/td\u003E\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\n","\u003Ch3\u003EClean up lab objects\u003C/h3\u003E\n","\u003Cp\u003ERun the following only if you created these objects for this lab and no longer need them:\u003C/p\u003E\n\u003Cpre\u003E\u003Ccode class=\"language-sql\"\u003EDROP AGENT IF EXISTS SEMANTIC_VIEWS_LAB.DEMO.MARKETS_DESK_AGENT;\nDROP SEMANTIC VIEW IF EXISTS SEMANTIC_VIEWS_LAB.DEMO.EQUITY_MARKET_SV;\nDROP SEMANTIC VIEW IF EXISTS SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_MARKET_SV;\nDROP VIEW IF EXISTS SEMANTIC_VIEWS_LAB.DEMO.EQUITY_OBSERVATIONS;\nDROP VIEW IF EXISTS SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_OBSERVATIONS;\nDROP VIEW IF EXISTS SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_VARIABLES;\n\u003C/code\u003E\u003C/pre\u003E\n","\u003Cp\u003EAsk the database owner to remove the lab schema and database if they contain no other work. Do not drop the imported Marketplace database or a shared warehouse as part of this cleanup.\u003C/p\u003E\n","\u003Ch2\u003EConclusion and Resources\u003C/h2\u003E\n","\u003Cp\u003EYou now have two semantic views with reviewed business meaning, and an agent that chooses between them and combines their results when a question spans both. All of it is defined in SQL you can review and version.\u003C/p\u003E\n","\u003Ch3\u003EWhat You Learned\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003EStart with a persona's questions and scope, not with every available table.\u003C/li\u003E\u003Cli\u003ESplit semantic views where the questions split.\u003C/li\u003E\u003Cli\u003EWrite semantic views in SQL, and keep each metric limited to the data it describes.\u003C/li\u003E\u003Cli\u003ECheck semantic view results against hand-written reference SQL.\u003C/li\u003E\u003Cli\u003EAdd synonyms, SQL generation instructions, and verified queries deliberately.\u003C/li\u003E\u003Cli\u003EConnect semantic views to an agent as Cortex Analyst tools with \u003Ccode\u003ECREATE AGENT\u003C/code\u003E.\u003C/li\u003E\u003C/ul\u003E\n","\u003Cp\u003ENext, adapt the question set to one domain in your own organisation. When you're ready to measure answer quality systematically, see Cortex Analyst and Cortex Agent evaluations.\u003C/p\u003E\n","\u003Ch3\u003ERelated Resources\u003C/h3\u003E\n\u003Cul\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view\"\u003ECREATE SEMANTIC VIEW\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/best-practices-modeling\"\u003EBest practices for modelling semantic views\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/querying\"\u003EQuerying semantic views\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-manage\"\u003ECreate and manage Cortex Agents\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst-evaluations\"\u003ECortex Analyst evaluations\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-evaluations\"\u003ECortex Agent evaluations\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/best-practices-dev\"\u003EDeveloping and deploying semantic views\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://docs.snowflake.com/en/user-guide/views-semantic/autopilot\"\u003ESemantic View Autopilot\u003C/a\u003E\u003C/li\u003E\u003Cli\u003E\u003Ca href=\"https://data-docs.snowflake.com/\"\u003ESnowflake Public Data documentation\u003C/a\u003E\u003C/li\u003E\u003C/ul\u003E"],"description":"","title":"Getting Started with Semantic Views and Cortex Agents",":type":"snowflake-site/components/contentfragment",":items":{},":itemsOrder":[],"elements":{"quickstartArticleBody":{"dataType":"string","title":"Quickstart Article Body","value":"## Overview\n\nA semantic view is an agreement about what the business means. Someone still needs to decide whether a price should be averaged, whether two terms mean the same thing, and which questions the model should answer. Generating the definition is the easy part; reviewing it is the work.\n\nIn this guide, you start with public data from Snowflake Marketplace. You create two semantic views, check them against hand-written SQL, refine their vocabulary and instructions, and connect both to a Cortex Agent through Cortex Analyst tools.\n\n**This guide uses SQL for every step.** Snowflake also offers other ways to do most of this work: Semantic View Autopilot and Semantic Studio in Snowsight for semantic views, the Snowsight agent editor for agents, and Cortex Code skills that generate definitions from a prompt. They produce the same objects. This guide mentions them where they apply but doesn't walk through them. SQL definitions are explicit, reviewable in a diff, and easy to put under version control.\n\n### Prerequisites\n\n- Comfort writing SQL, including joins and aggregates.\n- A Snowflake account with access to Cortex Analyst and Cortex Agents.\n- An administrator who can approve Marketplace acquisition and provision the lab privileges if you cannot do so yourself.\n\n### What You'll Learn\n\n- Scope a semantic view around a persona and a set of questions.\n- Write semantic views with `CREATE SEMANTIC VIEW` and refine them with `CREATE OR ALTER`.\n- Add synonyms, SQL generation instructions, and verified queries.\n- Check semantic view results against reference SQL.\n- Create an agent with two Cortex Analyst tools using `CREATE AGENT`.\n\n### What You'll Need\n\n- A SQL worksheet in Snowsight, or the [Snowflake CLI](https://docs.snowflake.com/en/developer-guide/snowflake-cli/installation/installation).\n- Access to [Snowflake Curated Data (Free)](https://app.snowflake.com/marketplace/listing/GZTSZ290BV255).\n- An existing, approved warehouse for the exercises.\n- A lab role with access to the imported data and permission to create a database, or a dedicated database provisioned by an administrator.\n\nThe lab role needs `USAGE` on the warehouse and the lab database and schema, plus `CREATE VIEW`, `CREATE SEMANTIC VIEW`, and `CREATE AGENT` on the lab schema, and the appropriate Cortex feature access. It also needs read access to the source data. See [Cortex Agent access control](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-setup).\n\nDo not change account-wide AI settings for this exercise without administrator approval. Model availability and any cross-region inference settings must meet your organization's requirements.\n\n\u003E The listing has no data subscription charge under its free tier, but warehouse queries and Cortex services incur charges. The free listing has a three-month data lag; this guide deliberately uses a historical period.\n\n### What You'll Build\n\n- Three small SQL views that scope the public source data without copying it.\n- `EQUITY_MARKET_SV` and `MORTGAGE_MARKET_SV`, two semantic views written in SQL.\n- `MARKETS_DESK_AGENT`, with one Cortex Analyst tool per semantic view.\n\n## Define the Questions\n\nYour persona is a **financial news writer** on a markets desk, looking back at 2024. The writer covers two beats: big tech stocks and borrowing costs. They need accurate historical figures, not investment recommendations or forecasts.\n\nThe two beats produce different questions, so each gets its own semantic view. This is the scoping rule to take away: keep data in one semantic view when the persona's questions combine it, and split where the questions split.\n\n| Topic | Questions it answers | Questions it does not answer |\n|---|---|---|\n| Equity market | Observed stock prices and Nasdaq trading volume for AAPL, MSFT, and AMZN during 2024 | Portfolio returns, dividends, buy/sell recommendations, mortgage rates |\n| Mortgage market | US national 15-year and 30-year fixed mortgage-rate observations during 2024 | Individual loan quotes, regional rates, stock prices |\n\nUse these questions to guide the build:\n\n1. What was AAPL's average post-market closing price in January 2024?\n2. What was MSFT's total Nasdaq trading volume in January 2024?\n3. Show the monthly average 30-year fixed mortgage rate for 2024, including its source unit.\n4. Compare the average 15-year and 30-year fixed mortgage rates in January 2024, keeping each series separate.\n5. What was the average rate in January 2024?\n\nThe fifth question is deliberately ambiguous. The desired behaviour is to ask which mortgage term the user means, rather than invent a default.\n\nSometimes the writer wants a recap that covers both beats. The agent can answer that by querying each semantic view separately and combining the results in one response. It does not join the two views, so questions that need row-level alignment between them, such as \"how did stocks move in the weeks when rates rose\", belong in a single semantic view instead.\n\nA **semantic view** defines business meaning. **Cortex Analyst** uses that meaning to generate SQL from a question. The **agent** chooses tools and presents their results. These are separate responsibilities.\n\n## Get Public Data\n\n### Acquire the listing\n\nYour role needs `CREATE DATABASE` and `IMPORT SHARE`, or you can use `ACCOUNTADMIN`. Run the commands as a person user whose profile includes a first name, last name, and email address. You can also get the listing from its [Marketplace page](https://app.snowflake.com/marketplace/listing/GZTSZ290BV255) in Snowsight.\n\n```sql\n-- Check that the listing is available in your region (is_ready_for_import = TRUE).\nDESCRIBE AVAILABLE LISTING GZTSZ290BV255;\n\n-- Only if is_ready_for_import is FALSE: request the listing and wait for it.\nCALL SYSTEM$REQUEST_LISTING_AND_WAIT('GZTSZ290BV255');\n\n-- Accept the listing terms, then create the imported database.\nCALL SYSTEM$ACCEPT_LEGAL_TERMS('DATA_EXCHANGE_LISTING', 'GZTSZ290BV255');\nCREATE DATABASE SNOWFLAKE_PUBLIC_DATA_FREE FROM LISTING 'GZTSZ290BV255';\n```\n\nReview the listing terms before you accept them. If accepting the terms with SQL fails, accept them on the listing page in Snowsight, then run the `CREATE DATABASE` statement. If your account already has the listing under another database name, replace `SNOWFLAKE_PUBLIC_DATA_FREE` consistently in the SQL below. See [Manage listings with SQL as a consumer](https://docs.snowflake.com/en/collaboration/consumer-listings-progaccess-examples).\n\nIf another role created the database, grant `IMPORTED PRIVILEGES` on it to your lab role. Do not create lab objects inside the imported database. It contains provider-managed, read-only data.\n\nConfirm the schema and object names:\n\n```sql\nSHOW VIEWS IN SCHEMA SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE;\n```\n\nThe source objects used in this guide are:\n\n- `STOCK_PRICE_TIMESERIES`: dated price and trading-volume observations.\n- `FREDDIE_MAC_HOUSING_TIMESERIES`: dated housing and mortgage observations.\n- `FREDDIE_MAC_HOUSING_ATTRIBUTES`: definitions and attributes for those housing variables.\n\nThe provider documents the [stock data](https://data-docs.snowflake.com/sources/stocks) and [Freddie Mac data](https://data-docs.snowflake.com/sources/freddie-mac).\n\n### Prepare the lab schema\n\n```sql\nSELECT CURRENT_ACCOUNT(), CURRENT_ROLE(), CURRENT_WAREHOUSE();\n\nCREATE DATABASE SEMANTIC_VIEWS_LAB;\nCREATE SCHEMA SEMANTIC_VIEWS_LAB.DEMO;\nUSE SCHEMA SEMANTIC_VIEWS_LAB.DEMO;\n```\n\nThese statements intentionally do not replace existing objects. If the database name is already taken, choose a unique lab name and update all subsequent SQL. If an administrator provisioned the database and schema, skip the two `CREATE` statements but keep the `USE SCHEMA`.\n\n### Scope the source data\n\nPublic time-series datasets often store different measures in a shared `VALUE` column. A price, a volume, and an interest rate are not interchangeable just because they are numeric.\n\nCreate three small SQL views to keep the exercise focused. These views filter and select columns; they do not copy the data.\n\n```sql\nCREATE VIEW SEMANTIC_VIEWS_LAB.DEMO.EQUITY_OBSERVATIONS AS\nSELECT\n    TICKER,\n    DATE AS OBSERVATION_DATE,\n    VARIABLE_NAME,\n    VALUE\nFROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.STOCK_PRICE_TIMESERIES\nWHERE TICKER IN ('AAPL', 'MSFT', 'AMZN')\n  AND VARIABLE_NAME IN ('Post-Market Close', 'Nasdaq Volume')\n  AND DATE \u003E= '2024-01-01'::DATE\n  AND DATE \u003C '2025-01-01'::DATE;\n\nCREATE VIEW SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_OBSERVATIONS AS\nSELECT\n    GEO_ID,\n    VARIABLE,\n    DATE AS OBSERVATION_DATE,\n    VALUE,\n    UNIT\nFROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.FREDDIE_MAC_HOUSING_TIMESERIES\nWHERE GEO_ID = 'country/USA'\n  AND DATE \u003E= '2024-01-01'::DATE\n  AND DATE \u003C '2025-01-01'::DATE\n  AND VARIABLE IN (\n      SELECT VARIABLE\n      FROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.FREDDIE_MAC_HOUSING_ATTRIBUTES\n      WHERE VARIABLE_NAME IN (\n          '15-Year Fixed Rate Mortgage Rate, National Average',\n          '30-Year Fixed Rate Mortgage Rate, National Average'\n      )\n  );\n\nCREATE VIEW SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_VARIABLES AS\nSELECT VARIABLE, VARIABLE_NAME, MEASURE, FREQUENCY\nFROM SNOWFLAKE_PUBLIC_DATA_FREE.PUBLIC_DATA_FREE.FREDDIE_MAC_HOUSING_ATTRIBUTES\nWHERE VARIABLE_NAME IN (\n    '15-Year Fixed Rate Mortgage Rate, National Average',\n    '30-Year Fixed Rate Mortgage Rate, National Average'\n);\n```\n\nPreview the selected data:\n\n```sql\nSELECT TICKER, VARIABLE_NAME, OBSERVATION_DATE, VALUE\nFROM SEMANTIC_VIEWS_LAB.DEMO.EQUITY_OBSERVATIONS\nORDER BY TICKER, VARIABLE_NAME, OBSERVATION_DATE\nLIMIT 12;\n\nSELECT\n    attributes.VARIABLE_NAME,\n    observations.OBSERVATION_DATE,\n    observations.VALUE,\n    observations.UNIT,\n    attributes.FREQUENCY\nFROM SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_OBSERVATIONS AS observations\nJOIN SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_VARIABLES AS attributes\n    ON observations.VARIABLE = attributes.VARIABLE\nORDER BY attributes.VARIABLE_NAME, observations.OBSERVATION_DATE\nLIMIT 12;\n```\n\n**Checkpoint:** Both queries return data. Look closely at the mortgage rows: the `UNIT` column says `Percent`, but the values are fractions, such as `0.066` for a 6.6% rate. The guide keeps the source values as they are and makes sure every answer reports them with their unit. Don't rescale them based on magnitude alone. The mortgage series is weekly, so a monthly average is an average of about four observations.\n\n## Create the Equity View\n\nStart with a single logical table. Relationships come in the second view.\n\n### Write the definition\n\n```sql\nCREATE OR REPLACE SEMANTIC VIEW SEMANTIC_VIEWS_LAB.DEMO.EQUITY_MARKET_SV\n  TABLES (\n    equity AS SEMANTIC_VIEWS_LAB.DEMO.EQUITY_OBSERVATIONS\n      COMMENT = 'Daily equity observations for AAPL, MSFT, and AMZN in 2024. One row per ticker, date, and series.'\n  )\n  FACTS (\n    equity.value AS VALUE\n      COMMENT = 'Observed value. Its meaning depends on variable_name: a price for Post-Market Close, a share count for Nasdaq Volume.'\n  )\n  DIMENSIONS (\n    equity.ticker AS TICKER\n      WITH SYNONYMS = ('stock', 'symbol')\n      COMMENT = 'Stock ticker symbol: AAPL, MSFT, or AMZN.',\n    equity.observation_date AS OBSERVATION_DATE\n      COMMENT = 'Trading date of the observation.',\n    equity.variable_name AS VARIABLE_NAME\n      COMMENT = 'Series name: Post-Market Close or Nasdaq Volume.'\n  )\n  METRICS (\n    equity.avg_post_market_close AS AVG(CASE WHEN VARIABLE_NAME = 'Post-Market Close' THEN VALUE END)\n      COMMENT = 'Arithmetic mean of non-null Post-Market Close observations, in the source price unit.',\n    equity.total_nasdaq_volume AS SUM(CASE WHEN VARIABLE_NAME = 'Nasdaq Volume' THEN VALUE END)\n      COMMENT = 'Sum of non-null Nasdaq Volume observations (shares traded).'\n  )\n  COMMENT = 'Historical equity prices and Nasdaq volume for AAPL, MSFT, and AMZN in 2024. No dividends, returns, forecasts, or mortgage rates.';\n```\n\nThe building blocks are:\n\n| Clause | Purpose in this example |\n|---|---|\n| `TABLES` | Maps the logical table `equity` to the scoped SQL view |\n| `FACTS` | Row-level values that metrics are built from |\n| `DIMENSIONS` | Ticker, trading date, and the source series name |\n| `METRICS` | Aggregations, each limited to one series |\n| `COMMENT` and `WITH SYNONYMS` | Business meaning and approved alternative terms; Cortex Analyst reads them |\n\nThe conditional `CASE` expressions are the important design decision. The source stores prices and volumes in the same `VALUE` column, so a plain `AVG(VALUE)` would average prices with share counts. Each metric includes only its own series.\n\nSemantic View Autopilot can generate a first draft like this from a table and a business description, and a Cortex Code skill can write one from a prompt. Either way, review the definition against the points above before you rely on it.\n\n### Check it against reference SQL\n\nA semantic view that deploys is syntactically valid. That doesn't make its calculations right. Compare a metric with SQL you wrote by hand:\n\n```sql\nSELECT 'reference' AS source, AVG(VALUE) AS avg_post_market_close\nFROM SEMANTIC_VIEWS_LAB.DEMO.EQUITY_OBSERVATIONS\nWHERE VARIABLE_NAME = 'Post-Market Close'\n  AND TICKER = 'AAPL'\n  AND OBSERVATION_DATE \u003E= '2024-01-01'::DATE\n  AND OBSERVATION_DATE \u003C '2024-02-01'::DATE\nUNION ALL\nSELECT 'semantic view', avg_post_market_close\nFROM SEMANTIC_VIEW(\n    SEMANTIC_VIEWS_LAB.DEMO.EQUITY_MARKET_SV\n    DIMENSIONS equity.ticker\n    METRICS equity.avg_post_market_close\n    WHERE equity.ticker = 'AAPL'\n      AND equity.observation_date \u003E= '2024-01-01'::DATE\n      AND equity.observation_date \u003C '2024-02-01'::DATE\n);\n```\n\n**Checkpoint:** Both rows show the same value. If they differ, inspect the metric expression and filters before continuing. Record the value as your reference answer for this run of the lab; historical data can be revised, so the guide doesn't hard-code it.\n\n## Refine the Equity View\n\n`CREATE OR ALTER SEMANTIC VIEW` changes an existing semantic view to match the statement. Any property you leave out is removed, so always submit the complete definition.\n\n### Add vocabulary and a verified query\n\nFor this exercise, the desk uses **extended-session close** to mean the provider's **Post-Market Close** series. This is a lab vocabulary decision, not a universal industry definition, so it goes into the metric as a synonym and its comment explains exactly what it means.\n\nYou also add a **verified query**: a question paired with SQL that you have checked. Cortex Analyst uses verified queries as examples when generating SQL, and when a question matches one exactly, the agent runs its SQL directly. In verified-query SQL, refer to a logical table by its name with a double-underscore prefix, such as `__equity`.\n\n```sql\nCREATE OR ALTER SEMANTIC VIEW SEMANTIC_VIEWS_LAB.DEMO.EQUITY_MARKET_SV\n  TABLES (\n    equity AS SEMANTIC_VIEWS_LAB.DEMO.EQUITY_OBSERVATIONS\n      COMMENT = 'Daily equity observations for AAPL, MSFT, and AMZN in 2024. One row per ticker, date, and series.'\n  )\n  FACTS (\n    equity.value AS VALUE\n      COMMENT = 'Observed value. Its meaning depends on variable_name: a price for Post-Market Close, a share count for Nasdaq Volume.'\n  )\n  DIMENSIONS (\n    equity.ticker AS TICKER\n      WITH SYNONYMS = ('stock', 'symbol')\n      COMMENT = 'Stock ticker symbol: AAPL, MSFT, or AMZN.',\n    equity.observation_date AS OBSERVATION_DATE\n      COMMENT = 'Trading date of the observation.',\n    equity.variable_name AS VARIABLE_NAME\n      COMMENT = 'Series name: Post-Market Close or Nasdaq Volume.'\n  )\n  METRICS (\n    equity.avg_post_market_close AS AVG(CASE WHEN VARIABLE_NAME = 'Post-Market Close' THEN VALUE END)\n      WITH SYNONYMS = ('extended-session close')\n      COMMENT = 'Arithmetic mean of non-null Post-Market Close observations, in the source price unit. In this lab, extended-session close means the provider Post-Market Close series. Not the regular-session close, adjusted close, or a return.',\n    equity.total_nasdaq_volume AS SUM(CASE WHEN VARIABLE_NAME = 'Nasdaq Volume' THEN VALUE END)\n      COMMENT = 'Sum of non-null Nasdaq Volume observations (shares traded on Nasdaq, not all-market volume).'\n  )\n  COMMENT = 'Historical equity prices and Nasdaq volume for AAPL, MSFT, and AMZN in 2024. No dividends, returns, forecasts, or mortgage rates.'\n  AI_VERIFIED_QUERIES (\n    aapl_avg_close_jan_2024 AS (\n      QUESTION 'What was AAPL''s average post-market closing price in January 2024?'\n      SQL 'SELECT ticker, AVG(CASE WHEN variable_name = ''Post-Market Close'' THEN value END) AS avg_post_market_close FROM __equity WHERE ticker = ''AAPL'' AND observation_date \u003E= ''2024-01-01'' AND observation_date \u003C ''2024-02-01'' GROUP BY ticker'\n    )\n  );\n```\n\nA synonym changes vocabulary, not the formula. If two terms mean different calculations, define separate metrics instead. Add synonyms only for terms your users actually use; a long generated list makes matching less precise, not more.\n\n### Review the stored definition\n\nRead the definition back as YAML to confirm what Snowflake stored:\n\n```sql\nSELECT SYSTEM$READ_YAML_FROM_SEMANTIC_VIEW(\n    'SEMANTIC_VIEWS_LAB.DEMO.EQUITY_MARKET_SV'\n) AS SEMANTIC_VIEW_YAML;\n```\n\nCheck that the synonym, the updated comments, and the verified query are present. The YAML form is useful for reviews and for tools that work with YAML; Semantic Studio in Snowsight shows the same definition visually. This guide keeps the SQL statement as the source of truth.\n\n### Keep filters explicit\n\nThe source SQL views enforce the lab's ticker, geography, and 2024 boundaries. The conditional metric expressions enforce which series contributes to each metric. Question-specific date filters further narrow the scope.\n\nDo not assume that a comment or an instruction restricts every query. Comments and instructions guide Cortex Analyst; they are not constraints or security controls. Inspect generated SQL to confirm that the required predicates are present.\n\n## Add the Mortgage View\n\nThe second topic is mortgage rates. Here the observations join to series descriptions, which gives you a relationship to review.\n\n### Check the join key\n\nA relationship assumes that the referenced key is unique. Check it before declaring it. This query must return no rows:\n\n```sql\nSELECT VARIABLE, COUNT(*) AS ROW_COUNT\nFROM SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_VARIABLES\nGROUP BY VARIABLE\nHAVING VARIABLE IS NULL OR COUNT(*) \u003E 1;\n```\n\nIf it returns rows, joining would duplicate observations. Fix the source view rather than hiding duplicates with `DISTINCT`.\n\n### Write the definition\n\n```sql\nCREATE OR REPLACE SEMANTIC VIEW SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_MARKET_SV\n  TABLES (\n    observations AS SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_OBSERVATIONS\n      COMMENT = 'Weekly US national fixed mortgage-rate observations in 2024.',\n    variables AS SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_VARIABLES\n      PRIMARY KEY (VARIABLE)\n      COMMENT = 'One row per mortgage series: 15-year and 30-year fixed.'\n  )\n  RELATIONSHIPS (\n    observations_to_variables AS observations (VARIABLE) REFERENCES variables\n  )\n  FACTS (\n    observations.value AS VALUE\n      COMMENT = 'Observed mortgage rate in the source unit.'\n  )\n  DIMENSIONS (\n    observations.observation_date AS OBSERVATION_DATE\n      COMMENT = 'Date of the weekly observation.',\n    observations.geo_id AS GEO_ID\n      COMMENT = 'Geography. Always country/USA in this lab.',\n    observations.unit AS UNIT\n      COMMENT = 'Source unit of the rate. Report it with every value.',\n    variables.variable_name AS VARIABLE_NAME\n      WITH SYNONYMS = ('mortgage term', 'mortgage series')\n      COMMENT = 'Mortgage series: 15-Year or 30-Year Fixed Rate Mortgage Rate, National Average.',\n    variables.measure AS MEASURE\n      COMMENT = 'Measure described by the series.',\n    variables.frequency AS FREQUENCY\n      COMMENT = 'Observation frequency of the series.'\n  )\n  METRICS (\n    observations.avg_observed_rate AS AVG(VALUE)\n      COMMENT = 'Arithmetic mean of available observations. Always group by variable_name and unit; never combine the 15-year and 30-year series.'\n  )\n  COMMENT = 'US national 15-year and 30-year fixed mortgage rates in 2024. Not loan quotes, APRs, regional rates, or forecasts.'\n  AI_SQL_GENERATION 'If a question asks about a rate without naming the 15-year or 30-year series, ask which one the user means. Never average the two series together. Preserve the source unit.'\n  AI_VERIFIED_QUERIES (\n    compare_15_30_jan_2024 AS (\n      QUESTION 'Compare the average 15-year and 30-year fixed mortgage rates in January 2024.'\n      SQL 'SELECT v.variable_name, o.unit, AVG(o.value) AS avg_observed_rate FROM __observations AS o JOIN __variables AS v ON o.variable = v.variable WHERE o.observation_date \u003E= ''2024-01-01'' AND o.observation_date \u003C ''2024-02-01'' GROUP BY v.variable_name, o.unit ORDER BY v.variable_name, o.unit'\n    )\n  );\n```\n\nThree things to review:\n\n- **The relationship** joins observations to variables on the `VARIABLE` key, not on the human-readable name. Each observation matches exactly one series row, which the key check confirmed.\n- **The metric** is a plain `AVG(VALUE)`. It is only meaningful per series and unit, which is why the comment says so.\n- **`AI_SQL_GENERATION`** gives Cortex Analyst instructions that apply to every question on this view. Here it handles the ambiguous \"rate\" question and protects against mixing the two series.\n\n### Check the second view\n\n```sql\nSELECT\n    attributes.VARIABLE_NAME,\n    observations.UNIT,\n    AVG(observations.VALUE) AS AVG_OBSERVED_RATE\nFROM SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_OBSERVATIONS AS observations\nJOIN SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_VARIABLES AS attributes\n    ON observations.VARIABLE = attributes.VARIABLE\nWHERE observations.OBSERVATION_DATE \u003E= '2024-01-01'::DATE\n  AND observations.OBSERVATION_DATE \u003C '2024-02-01'::DATE\nGROUP BY attributes.VARIABLE_NAME, observations.UNIT\nORDER BY attributes.VARIABLE_NAME, observations.UNIT;\n\nSELECT * FROM SEMANTIC_VIEW(\n    SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_MARKET_SV\n    DIMENSIONS variables.variable_name, observations.unit\n    METRICS observations.avg_observed_rate\n    WHERE observations.observation_date \u003E= '2024-01-01'::DATE\n      AND observations.observation_date \u003C '2024-02-01'::DATE\n)\nORDER BY variable_name, unit;\n```\n\n**Checkpoint:** Both queries return two rows, one per series, with matching values and units.\n\n## Create the Agent\n\nThe agent receives a question, decides which tool to call, and writes the answer. Each Cortex Analyst tool points to one semantic view. Tool descriptions drive tool choice, so make each one say what the tool covers and what it doesn't.\n\nReplace `COMPUTE_WH` with your approved warehouse. You can also build or edit the same agent in the Snowsight agent editor.\n\n```sql\nCREATE OR REPLACE AGENT SEMANTIC_VIEWS_LAB.DEMO.MARKETS_DESK_AGENT\n  COMMENT = 'Answers a markets desk''s historical questions using a small 2024 public-data sample. Not an investment adviser, forecasting service, or live market feed.'\n  PROFILE = '{\"display_name\": \"Markets Desk Assistant\"}'\n  FROM SPECIFICATION\n  $$\n  models:\n    orchestration: auto\n\n  instructions:\n    orchestration: |\n      Use EQUITY_MARKET_SV for equity prices and Nasdaq trading volume.\n      Use MORTGAGE_MARKET_SV for national fixed mortgage-rate observations.\n      Use the tools for data-backed answers; do not supply numbers from memory.\n\n      Ask for clarification when a rate question does not identify the mortgage\n      term. Keep different mortgage series and source units in separate groups.\n      If a requested calculation, instrument, or date is outside the configured\n      data, explain the limitation instead of substituting another measure.\n\n      If a question covers both stocks and mortgage rates, query each tool\n      separately, then combine the results in one summary. Attribute each figure\n      to its source. Do not infer relationships, correlations, or causes between\n      the two topics.\n    response: |\n      State the measure, period, ticker or mortgage series, and source unit.\n      Distinguish a period average from a value at a particular date.\n      Report mortgage values exactly as returned with their source unit; do not rescale them.\n      Explain missing data without treating it as zero.\n      Keep responses concise and do not give investment recommendations.\n\n  tools:\n    - tool_spec:\n        type: \"cortex_analyst_text_to_sql\"\n        name: \"EQUITY_MARKET_SV\"\n        description: \u003E-\n          Use for observed equity prices and Nasdaq trading volume for AAPL, MSFT,\n          and AMZN in 2024. Supports average Post-Market Close, also called\n          extended-session close in this lab, and total Nasdaq volume.\n          Does not answer mortgage-rate, dividend, portfolio-return, or forecast questions.\n    - tool_spec:\n        type: \"cortex_analyst_text_to_sql\"\n        name: \"MORTGAGE_MARKET_SV\"\n        description: \u003E-\n          Use for US national 15-year and 30-year fixed mortgage-rate observations\n          in 2024. Supports averages over available observations, grouped separately\n          by mortgage series and source unit. Does not answer equity-price questions,\n          individual loan quotes, regional mortgage rates, or forecasts.\n\n  tool_resources:\n    EQUITY_MARKET_SV:\n      semantic_view: \"SEMANTIC_VIEWS_LAB.DEMO.EQUITY_MARKET_SV\"\n      execution_environment:\n        type: \"warehouse\"\n        warehouse: \"COMPUTE_WH\"\n    MORTGAGE_MARKET_SV:\n      semantic_view: \"SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_MARKET_SV\"\n      execution_environment:\n        type: \"warehouse\"\n        warehouse: \"COMPUTE_WH\"\n  $$;\n\nDESCRIBE AGENT SEMANTIC_VIEWS_LAB.DEMO.MARKETS_DESK_AGENT;\n```\n\nThe specification has three parts:\n\n- **`instructions.orchestration`** guides planning: which tool to use, when to ask for clarification, and how to handle questions that span both topics.\n- **`instructions.response`** guides how the answer is written.\n- **`tools` and `tool_resources`** declare each Cortex Analyst tool and connect it to its semantic view and warehouse.\n\nThese instructions guide behaviour; they don't replace access controls or testing. See [Create and manage agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-manage) for the full specification format.\n\n**Checkpoint:** `DESCRIBE AGENT` shows both tools and their semantic views. You can ask the agent questions in the Snowsight agent playground, or with SQL through `SNOWFLAKE.CORTEX.DATA_AGENT_RUN`.\n\n## Troubleshoot and Clean Up\n\n### Troubleshooting\n\n| Symptom | Check |\n|---|---|\n| The listing cannot be acquired | Role privileges, user profile details, listing availability, and terms |\n| A source query returns no rows | Imported database and schema names, series names, and historical coverage |\n| `CREATE SEMANTIC VIEW` fails | Clause order (`FACTS` before `DIMENSIONS`), logical table names, and doubled single quotes inside string literals |\n| A metric looks inflated | Mixed series, duplicate keys in a referenced table, or an incorrect relationship |\n| Mortgage rates look 100 times too small | The source stores fractions with unit `Percent`; report values as returned with their unit |\n| The agent selects the wrong tool | Make tool descriptions more distinct and retest |\n| The agent reports an inaccessible tool | The caller's role, warehouse access, semantic view access, and underlying data privileges |\n\n### Clean up lab objects\n\nRun the following only if you created these objects for this lab and no longer need them:\n\n```sql\nDROP AGENT IF EXISTS SEMANTIC_VIEWS_LAB.DEMO.MARKETS_DESK_AGENT;\nDROP SEMANTIC VIEW IF EXISTS SEMANTIC_VIEWS_LAB.DEMO.EQUITY_MARKET_SV;\nDROP SEMANTIC VIEW IF EXISTS SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_MARKET_SV;\nDROP VIEW IF EXISTS SEMANTIC_VIEWS_LAB.DEMO.EQUITY_OBSERVATIONS;\nDROP VIEW IF EXISTS SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_OBSERVATIONS;\nDROP VIEW IF EXISTS SEMANTIC_VIEWS_LAB.DEMO.MORTGAGE_VARIABLES;\n```\n\nAsk the database owner to remove the lab schema and database if they contain no other work. Do not drop the imported Marketplace database or a shared warehouse as part of this cleanup.\n\n## Conclusion and Resources\n\nYou now have two semantic views with reviewed business meaning, and an agent that chooses between them and combines their results when a question spans both. All of it is defined in SQL you can review and version.\n\n### What You Learned\n\n- Start with a persona's questions and scope, not with every available table.\n- Split semantic views where the questions split.\n- Write semantic views in SQL, and keep each metric limited to the data it describes.\n- Check semantic view results against hand-written reference SQL.\n- Add synonyms, SQL generation instructions, and verified queries deliberately.\n- Connect semantic views to an agent as Cortex Analyst tools with `CREATE AGENT`.\n\nNext, adapt the question set to one domain in your own organisation. When you're ready to measure answer quality systematically, see Cortex Analyst and Cortex Agent evaluations.\n\n### Related Resources\n\n- [CREATE SEMANTIC VIEW](https://docs.snowflake.com/en/sql-reference/sql/create-semantic-view)\n- [Best practices for modelling semantic views](https://docs.snowflake.com/en/user-guide/views-semantic/best-practices-modeling)\n- [Querying semantic views](https://docs.snowflake.com/en/user-guide/views-semantic/querying)\n- [Create and manage Cortex Agents](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-manage)\n- [Cortex Analyst evaluations](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-analyst-evaluations)\n- [Cortex Agent evaluations](https://docs.snowflake.com/en/user-guide/snowflake-cortex/cortex-agents-evaluations)\n- [Developing and deploying semantic views](https://docs.snowflake.com/en/user-guide/views-semantic/best-practices-dev)\n- [Semantic View Autopilot](https://docs.snowflake.com/en/user-guide/views-semantic/autopilot)\n- [Snowflake Public Data documentation](https://data-docs.snowflake.com/)\n","multiValue":false,":type":"text/x-markdown"},"quickstartArticleLogoImage":{"dataType":"string","title":"Quickstart Article Logo Image","multiValue":false,":type":"text/plain"}},"elementsOrder":["quickstartArticleBody","quickstartArticleLogoImage"],"isDeveloperGuidesPage":false,"model":"snowflake-site/models/quickstart-article"},"flexible_column_cont":{"id":"flexible-column-container-db300874bb","type":"2-column-75-25","alignColumns":"top","containerMaxWidth":"extra-large","topPadding":"none","bottomPadding":"none","spaceBetween":"none","reverseOnMobile":false,"carouselOnMobile":false,"backgroundImageOption":"none","flexible_column_content_container_1":{"layout":"SIMPLE","id":"container-66f8cc229d",":type":"snowflake-site/components/flexible-column-container/flexible-column-content-container",":items":{"quickstart_last_modi":{"id":"quickstart-last-modified-3227185987","icon":{"id":"icon","icon":"calendar",":type":"snowflake-site/components/icon","appliedCssClassNames":"snowflake-icon-blue"},"lastModifiedDatePrefix":"Updated","lastModifiedDate":"2026-11-06",":type":"snowflake-site/components/quickstart/quickstart-last-modified","appliedCssClassNames":"snowflake-responsive-component-top-padding-small"},"text":{"id":"text-1029b01048","additionalClasses":"qs-disclaimer-text","text":"\u003Cp\u003E\u003Cspan style=\"color: #666;\"\u003EThis content is provided as is, and is not maintained on an ongoing basis. 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