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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A Streamlit application developer wants to use AI_COMPLETE (the latest version of COMPLETE (SNOWFLAKE. CORTEX)) to process customer feedback. The goal is to extract structured information, such as the customer's sentiment, product mentioned, and any specific issues, into a predictable JSON format for immediate database ingestion. Which configuration of the AI COMPLETE function call is essential for achieving this structured output requirement?

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

2. A development team plans to utilize Snowpark Container Services (SPCS) for deploying a variety of AI/ML workloads, including custom LLMs and GPU-accelerated model training jobs. They are in the process of creating a compute pool and need to select the appropriate instance families and configurations. Which of the following statements about 'CREATE COMPUTE POOL' in SPCS are accurate?

A) For cost optimization, 'AUTO SUSPEND SECS = 0' should be used to prevent automatic suspension of the compute pool, as suspension and resumption incur minimum billing durations.
B) Snowpark-optimized warehouses are the recommended compute pool type for all large-scale ML training workloads within SPCS due to their enhanced memory limits and CPU architectures.
C) Setting 'AUTO RESUME = TRUE ensures that the compute pool automatically starts when a service or job is submitted to it, rather than requiring manual resumption.
D) The 'MIN NODES' and 'MAX NODES parameters define the scaling range for the compute pool, and Snowflake automatically scales the pool within this range based on workload demand.
E) To support GPU-accelerated LLM inference and training, the 'INSTANCE_FAMILY' must be selected from a type starting with 'GPU' (e.g.,

3. 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 E
B) Option B
C) Option C
D) Option A
E) Option D

4. A data team is designing a new Cortex Analyst application and wants to ensure optimal performance, accuracy, and user experience for text-to-SQL conversions. They are particularly interested in how custom instructions interact with other semantic model features and LLM functionalities. Which of the following statements about using in Cortex Analyst are accurate?

A) Custom instructions are primarily used to define new logical tables or dimensions within the semantic model, effectively extending the data model at runtime.
B) When both 'custom_instructionS and a highly relevant 'verified_query' exist for a user's question, Cortex Analyst will always prioritize the directives from the 'custom_instructions' over the SQL provided in the 'verified_querv'.
C) The presence of 'custom_instructions' in a semantic model can potentially increase the token count for Cortex Analyst requests, as the instructions are passed as additional context to the LLM.
D) Using detailed 'custom_instructions' can help mitigate issues where the LLM might struggle with domain-specific terminology or complex business logic not explicitly defined in column descriptions.
E) The 'custom_instructions' in a semantic model directly influence the underlying Large Language Model (LLM) to generate SQL queries that align with specified business context or formatting preferences.

5. A data scientist has developed a Hugging Face sentence transformer model for semantic search and needs to deploy it for GPU- powered inference using Snowpark Container Services (SPCS) in Snowflake. They've already trained the SentenceTransformer model locally. Which of the following statements correctly describe essential considerations for logging and deploying this model, ensuring it leverages GPU resources and appropriate dependencies?

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

Solutions: