Snowflake GES-C01

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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A machine learning engineer is building a product recommendation system in Snowflake that uses item embeddings and customer query embeddings for similarity matching. They plan to use the VECTOR_L1_DISTANCE function to find the closest products to a user's query. Which statement accurately describes the cost and data type considerations for this approach?

A) Storing the item and query embeddings in
B) Both

C) The
D) Executing the

E) The maximum dimension for a

2. An organization is planning to implement a new Retrieval Augmented Generation (RAG) application and has chosen Snowflake Cortex Search as its core retrieval engine. To effectively manage their budget, the finance and data teams need a clear understanding of the various cost components associated with deploying and operating a Cortex Search Service. Which of the following represent distinct cost categories directly attributable to the deployment and ongoing operation of a Snowflake Cortex Search Service?

A) Storage for the materialized source data and the optimized search index data structures within the Snowflake account.
B) Virtual warehouse compute used for refreshing the search service's index and processing base object changes.
C) Cloud Services compute for monitoring underlying base objects for changes to trigger search service refreshes.
D) ' Services compute specifically for generating vector embeddings of text data during the indexing and update processes.
E) Compute costs for LLM inference (e.g., SNOWFLAKE.CORTEX.COMPLETE) when the RAG application uses the retrieved context to generate responses.

3. An operations team is investigating an issue with a generative AI application powered by Snowflake Cortex Analyst, where users reported unexpected behavior in generated SQL. To diagnose the problem, they examine the detailed event logs captured by Snowflake AI Observability. Which categories of information can they expect to find in these event tables to assist their investigation?

A) The exact SQL queries that Cortex Analyst generated in response to user questions.
B) The complete request and response bodies associated with the application's execution steps.
C) The full text of the natural language questions submitted by the users.
D) Any error messages or warnings that occurred during the processing of the request.
E) Real-time CPU and memory usage statistics for the Snowflake virtual warehouse executing the LLM inference.

4. An administrator has configured the 'CORTEX MODELS ALLOWLIST' parameter to only permit the 'mistral-large? model at the account level. A user with the 'PUBLIC' role, which has been granted 'SNOWFLAKE.CORTEX USER and 'SNOWFLAKE."CORTEX- MODEL-ROLE-LLAMA3.1-70B"' , attempts to execute several 'AI_COMPLETE queries. Which of the following queries will successfully execute?

A)

B)

C)

D)

E)

5. A developer is building a conversational application using Snowflake Cortex Analyst and is interacting with it via the REST API. The application needs to support multi-turn conversations where users can ask follow-up questions. Which of the following statements accurately describe how to maintain conversational state and interact with the Cortex Analyst REST API for a multi-turn experience?

A) For performance optimization, only the most recent user prompt and the immediate preceding analyst response should be passed in the 'messages' array for follow-up questions.
B) To maintain state, the application must construct a 'messages' array for each new prompt, including all prior 'user' prompts and 'analyst' responses in chronological order, and send it with every API call.
C) A special 'RESET_CONVERSATION' API endpoint should be called periodically to clear the LLM's internal context and manage token consumption for long conversations.
D) The Cortex Analyst REST API automatically retains the full conversation history for each user session, eliminating the need to explicitly pass previous messages.
E) The argument, when using the 'COMPLETE function (or its API equivalent), must be a string for single-turn conversations but an array of objects for multi-turn conversations, with each object containing 'role' and 'content'.

Solutions: