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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineering team is deploying Snowflake Cortex Analyst to enable natural language queries over their structured SALES_DATA table, which includes columns like PRODUCT_CATEGORY, SALES_AMOUNT, and ORDER_DATE. To maximize the accuracy and trustworthiness of responses for business users, which of the following practices should the team implement when configuring their semantic model?

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

2. A data engineering team needs to configure their Snowflake environment to process documents using AI_PARSE_DOCUMENT and generate text embeddings using EMBED_TEXT_1024 with the voyage-multilingual-2 model. Their Snowflake account is in a region where these specific capabilities or models are only available via cross-region inference. The team needs to ensure these functions work correctly without constant region-specific model selection. Which of the following is the correct configuration action and an important consideration?

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

3. An enterprise is deploying a new RAG application using Snowflake Cortex Search on a large dataset of customer support tickets. The operations team is concerned about managing compute costs and ensuring efficient index refreshes for the Cortex Search Service, which needs to be updated hourly. Which of the following considerations and configurations are relevant for optimizing cost and performance of the Cortex Search Service in this scenario?

A) For optimal performance and cost efficiency, Snowflake recommends using a dedicated warehouse of size no larger than MEDIUM for each Cortex Search Service.
B) CHANGE_TRACKING
C) The primary cost driver for Cortex Search is the number of search queries executed against the service, with the volume of indexed data (GBImonth) having a minimal impact on overall billing.
D) The

E) For embedding text, selecting a model like

4. A machine learning engineering team is evaluating two different configurations of a Retrieval Augmented Generation (RAG) application. uses for generation, while uses 'mistral-7b' with a refined prompt for the same task. They aim to compare the and 'groundedness' of the generated responses, as well as the efficiency of context retrieval. Which of the following steps are crucial for setting up AI Observability in Snowflake to facilitate a meaningful side-by-side comparison and assess these specific metrics?

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

5. A Gen AI developer has a Document AI pipeline that uses a query with 'GET PRESIGNED URL' to process multi-page PDF documents. Despite the internal stage being correctly set up with 'SNOWFLAKE SSE' encryption and the model build being published, they observe inconsistent results. Some documents result in a Received HTTP 403 response for presigned URL. URL may be expired.
error, while other documents (containing complex diagrams and dense text in an unsupported language like Korean) are processed, but the extracted information is often incomplete or inaccurate.
Which two factors are most likely contributing to these observed issues?

A) The default expiration time for the 'GET PRESIGNED URL' function is causing some URLs to expire before the Document AI model can process them.
B) The role lacks the 'EXECUTE TASK' privilege, preventing the scheduled pipeline tasks from running.
C) The documents are in an unsupported language or exceed the maximum page length of 125 pages, causing extraction failures or inaccuracies.
D) The '!PREDICT method is being called with an outdated model build version instead of the latest one, leading to performance degradation.
E) The Document AI model is returning answers longer than its limit of 512 tokens for entity extraction or 2048 tokens for table extraction.

Solutions: