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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineering team is building a Retrieval Augmented Generation (RAG) pipeline that heavily relies on 'SNOWFLAKE.CORTEX.EMBED_TEXT 768' to process millions of documents daily. They need to optimize for both cost and retrieval quality. Which of the following statements are true regarding the cost and performance of 'EMBED_TEXT 768' in Snowflake? (Select all that apply)

A) For optimal retrieval quality in RAG scenarios, text should be split into chunks of no more than 512 tokens before being passed to 'EMBED TEXT 768', even if the model supports a larger context window.
B) The 'EMBED TEXT 768' function, regardless of the 768-dimension model used, has a fixed cost of 1.50 Credits per one million Tokens processed.
C) The 'snowflake-arctic-embed-m-vl .5 model, used by 'EMBED TEXT 768', has a context window of 512 tokens, and texts exceeding this length are truncated before embedding.
D) To minimize costs for ' EMBED_TEXT 768 operations, it is recommended to execute queries using a smaller virtual warehouse (no larger than MEDIUM), as larger warehouses do not improve performance for these functions.
E) The 'EMBED_TEXT 768' function is billed based on the number of 'output tokens' generated by the embedding model, as this represents the computational complexity of the vector.

2. An ML Engineer is logging a custom PyCaret model to the Snowflake Model Registry, with the intention of deploying it to Snowpark Container Services (SPCS) for GPU-powered inference. The PyCaret model is wrapped in a 'custom_model.ModelContext'. Which of the following statements correctly describe the considerations for the call and the model's environment?

A) Option B
B) Option E
C) Option D
D) Option C
E) Option A

3. A data engineering team is designing a Snowflake data pipeline to automatically enrich a 'customer issues' table with product names extracted from raw text-based 'issue_description' columns. They want to use a Snowflake Cortex function for this extraction and integrate it into a stream and task-based pipeline. Given the 'customer_issues' table with an 'issue_id' and (VARCHAR), which of the following SQL snippets correctly demonstrates the use of a Snowflake Cortex function for this data enrichment within a task, assuming is a stream on the 'customer issues' table?

A) Option B
B) Option E
C) Option D
D) Option C
E) Option A

4. A data analyst is working with a table named ARTICLE_CONTENT that contains a column (VARCHAR) storing lengthy English articles. They need to generate a concise summary for each article. The analyst plans to use the SNOWFLAKE. CORTEX. SUMMARIZE function. Which of the following accurately describes the syntax and the expected data type of the result for a single article summary?

A) The query

B) The query

C) The query

D) The query

E) The query

5. A data platform architect is evaluating the integration of 'SNOWFLAKE.CORTEX.TRANSLATE into several automated data pipelines. One pipeline involves real-time translation of messages for a chat application, while another is for batch processing of archived documents. The architect is considering various Snowflake features for orchestration and deployment. Which of the following considerations about 'SNOWFLAKE.CORTEX.TRANSLATE is accurate?

A) The 'TRANSLATE' function can be seamlessly integrated into a dynamic table's ' SELECT statement to provide continuous, automated translation with minimal configuration.
B) When using 'TRANSLATE within a Snowpark Python User-Defined Function (UDF), the raw text data must be explicitly moved out of Snowflake's network boundary to the underlying LLM service for translation.
C) To manage potential failures in a production pipeline, 'SNOWFLAKCORTEX.TRANSLATE should be wrapped in 'TRY COMPLETE' for robust error handling, returning NULL on failure instead of an error.
D) If 'TRANSLAT' is not natively available in the account's primary Snowflake region, cross-region inference cannot be enabled, thus preventing its use.
E) The Snowflake managed model used by the 'TRANSLATE' function has a context window of 4,096 tokens, meaning texts longer than this will be truncated before translation.

Solutions: