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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineer is developing a Snowflake Cortex LLM application that processes sensitive customer feedback. To ensure that generated responses from the 'COMPLETE' function are filtered for potentially unsafe or harmful content, they need to enable Cortex Guard. Which of the following SQL 'COMPLETE function calls correctly demonstrates the enablement of Cortex Guard with the default unsafe response message?

A)

B)

C)

D)

E)

2. A data engineering team has developed a Python-based generative AI application and instrumented its key functions using the TruLens SDK. Their next step is to register this application with Snowflake AI Observability to initiate evaluation runs and capture application traces within Snowflake.

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

3. An 'ACCOUNTADMIN' has configured the 'CORTEX MODELS ALLOWLIST parameter to allow only the 'mistral-large? model. A developer, whose role has been granted 'SNOWFLAKE.CORTEX USER and the specific application role 'SNOWFLAKE."CORTEX- MODEL-ROLE-LLAMA3.1-70B"' , subsequently accesses the Cortex LLM Playground. Which models would be available for selection and successful inference by this user within the Playground?

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

4. A data scientist is optimising a Cortex Analyst application to improve the accuracy of literal searches within user queries, especially for high-cardinality dimension values. They decide to integrate Cortex Search for this purpose. Which of the following statements are true about this integration and the underlying data types in Snowflake? (Select all that apply)

A) The "VECTOR data type in Snowflake, used to store embeddings generated for Cortex Search, is fully supported as a clustering key in standard tables and as a primary key in hybrid tables to accelerate vector similarity searches.
B) For optimal RAG retrieval performance with Cortex Search, it is generally recommended to split text into chunks of no more than 512 tokens, even when using embedding models with larger context windows such as 'snowflake-arctic-embed-l-v2.0-8k'.
C) Cortex Search Services, when configured as a source for Snowflake dynamic tables, automatically refresh their search index with continuous data updates, maintaining low-latency search results.
D) To integrate Cortex Search with a logical dimension, the semantic model YAML must include a block within the dimension's definition, specifying the service name and optionally a 'literal_column' .
E) The cost for embedding data into a Cortex Search Service is primarily incurred per output token generated by the embedding model, as these represent the final vector embeddings, rather than input tokens.

5. A data operations team is attempting to scale up their Document AI automated pipeline. They are using a Snowflake Task to process a large volume of daily scanned invoices and receipts, which are stored in an internal stage 'financial_docs stage'. The current processing involves documents that are frequently around 75 MB each, and often there are batches exceeding 1 ,200 documents in a single day. The pipeline is failing consistently. Which of the following factors could be contributing to the failures in this Document AI automated pipeline? (Select all that apply.)

A) The individual document size (75 MB) exceeds the maximum supported file size for Document AI.
B) The Snowflake Task is configured as a 'SERVERLESS TASK, which is not supported by Document AI.
C) The was created without specifying 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.
D) The number of documents in a single daily batch (exceeding 1 ,200) surpasses Document AI's processing limit per query.
E) The account role executing the task lacks the 'SNOWFLAKCORTEX USER database role, which is a prerequisite for Document AI functions.

Solutions: