Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data platform administrator needs to retrieve a consolidated overview of credit consumption for all Snowflake Cortex AI functions (e.g., LLM functions, Document AI, Cortex Search) across their entire account for the past week. They are interested in the aggregated daily credit usage rather than specific token counts per query. Which Snowflake account usage views should the administrator primarily leverage to gather this information?

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

2. A data scientist is tasked with improving the accuracy of an LLM-powered chatbot that answers user questions based on internal company documents stored in Snowflake. They decide to implement a Retrieval Augmented Generation (RAG) architecture using Snowflake Cortex Search. Which of the following statements correctly describe the features and considerations when leveraging Snowflake Cortex Search for this RAG application?

A) To create a Cortex Search Service, one must explicitly specify an embedding model and manually manage its underlying infrastructure, similar to deploying a custom model via Snowpark Container Services.
B) Enabling change tracking on the source table for the Cortex Search Service is optional; the service will still refresh automatically even if change tracking is disabled.
C) The

D) Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
E) For optimal search results with Cortex Search, source text should be pre-split into chunks of no more than 512 tokens, even when using models with larger context windows like

3. A financial institution uses Snowflake Cortex LLM functions to process customer feedback. They initially used SNOWF LAKE .CORTEX.SENTIMENT for general sentiment analysis. Now, they need to extract specific sentiment categories (e.g., 'service_quality', 'product_pricing') and the sentiment for each, expecting the output in a structured JSON format for automated downstream processing. Which AI_COMPLETE configuration best addresses their new requirement while considering cost-efficiency and output reliability?

A)

B)

C)

D)

E)

4. An ML Engineer has developed a custom PyTorch model for GPU-powered inference and successfully built an OCI-compliant image locally. They now need to push this image to a Snowflake image repository and configure a Snowpark Container Service to use it. The Snowflake account identifier is my org_name_my_account_id_prod. Which set of commands correctly demonstrates tagging the local image and pushing it to the repository?

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

5. A security auditor needs to access and analyze logs generated by Snowflake AI Observability for compliance auditing and to track the activity of generative AI applications. They need to understand how to reliably query this data and its temporal characteristics within Snowflake. Which of the following statements accurately describes the access and characteristics of this logged data?

A) Detailed request and response bodies, along with the generated SQL, are stored and can be directly queried using standard SQL.
B) Access to these detailed event tables is implicitly granted to roles holding the SNOWFLAKE. CORTEX_USER database role and the AI_OBSERVABILITY_EVENTS_LOOKUP application role.
C) The logs are automatically purged after 7 days of being recorded, requiring a separate process for long-term data retention.
D) Logs are exclusively available for analysis through pre-built dashboards in Snowsight and cannot be accessed via direct SQL queries.
E) Logged data from AI Observability's event tables becomes visible within a small latency, typically 1-2 minutes, after a request is made.

Solutions: