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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A global analytics firm is developing a Retrieval Augmented Generation (RAG) system in Snowflake to answer customer queries across a large repository of technical documentation, which includes documents in English, German, and Spanish. They are looking to use a Snowflake Cortex embedding model to convert document chunks into vector embeddings for their Cortex Search Service. Which of the following considerations are critical when selecting an appropriate embedding model to optimize for both query relevance and cost-efficiency for their multilingual RAG application? (Select all that apply)

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

2. A security administrator needs to manage access to Snowflake Cortex LLM functions and models, and ensure compliant usage across different regions for various teams. Which configuration options and parameters are relevant for these requirements?

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

3. An engineering team is building an advanced analytics pipeline where daily customer activity vectors (each 512 dimensions) are compared against a set of known activity patterns using VECTOR_L2_DISTANCE The pipeline is orchestrated using Snowflake Tasks. Which operational best practice or limitation should the team consider when processing these vector distances at scale?

A) For optimal performance of
B) The
C) Snowflake recommends using smaller warehouses (no larger than MEDIUM) for executing queries that call Cortex AI SQL functions, as larger warehouses do not necessarily increase performance, and this guidance extends to vector similarity functions.
D) When embedding external data into the Snowflake environment for

E) To handle potential errors from

4. An operations team at a company is implementing a robust governance framework to monitor and optimize the costs associated with their Snowflake Cortex LLM function usage. They need to identify which functions are driving the highest token consumption and overall credit usage to pinpoint areas for cost reduction. Which of the following monitoring tools or methods are appropriate for gaining these insights into Cortex LLM function costs and token consumption?

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

5. A new data analyst is trying to incorporate sentiment analysis using SNOWFLAKE. CORTEX. SENTIMENT within a Snowflake data pipeline that uses dynamic tables. They execute the following SQL to create a dynamic table for daily sentiment aggregation:

However, this operation fails. Which of the following is the most direct reason for the failure of this specific setup?

A) The CORTEX_USER database role was not granted to the analyst's role, preventing the execution of Cortex functions.
B) SNOWFLAKE. CORTEX. SENTIMENT and other Snowflake Cortex functions are currently incompatible with dynamic tables.
C) The TARGET_LAG for dynamic tables must be explicitly set to '1 day' or longer when integrating with Cortex functions.
D) The review_content column, if containing non-English text, would cause the SENTIMENT function to fail outright rather than produce inaccurate results.
E) The warehouse my_analytics_wh is likely not a Snowpark-optimized warehouse, which is a requirement for Cortex functions within dynamic tables.

Solutions: