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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. An analytics team needs to use various Snowflake Cortex LLM functions and wants to understand the cost implications and governance mechanisms for controlling model access and usage. The "ACCOUNTADMIN' has restricted general LLM access using the 'CORTEX_MODELS_ALLOWLIST' parameter. The team is particularly interested in how model complexity and specific function calls impact billing and how access can be managed effectively for different user groups.

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

2. An ML engineer is planning a fine-tuning project for a
llama3.1-8b
model to summarize long customer support tickets. They are considering the impact of dataset size and max_epochs on cost and performance, as well as the behavior of the fine-tuned model for inference. Which statements about cost and performance in Snowflake Cortex Fine-tuning are true? (Select all that apply)

A) D When fine-tuning a
B) For large fine-tuning jobs with substantial datasets, particularly when exceeding millions of rows, utilizing Snowpark-optimized warehouses is recommended for improved performance during the training phase.
C) The compute cost for fine-tuning is primarily determined by multiplying the number of input tokens in the training data by the number of epochs trained.
D) The cost for inferencing with a fine-tuned model using the
E) For optimal cost efficiency, especially with smaller datasets, the

3.

A)

B) The USAGE privilege on the specific database and schema where the Streamlit application and its underlying data tables are located.
C) The CREATE COMPUTE POOL privilege to provision resources for the Streamlit application.
D) The ACCOUNTADMIN role to ensure unrestricted access to all Snowflake Cortex features.
E)

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 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.
B) 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' .
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) 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.
E) 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'.

5. An AI engineer is building an automated pipeline in Snowflake that processes various types of textual data using Cortex AI functions. To ensure the pipeline's stability and avoid failures due to exceeding LLM context windows, they integrate SNOWFLAKE.CORTEX.COUNT_TOKENS and TRY_COMPLETE
. Consider the following code snippets and statements about context window management in Snowflake Cortex.

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

Solutions: