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SnowPro Practice Exam: Gen AI Free Sample Questions

Description

Covers Snowflake's generative AI principles and open-source models, Cortex LLM functions for retrieval and prompt engineering, access control and cost governance, and document parsing pipelines.

12 free sample questions150 in the full practice test

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GES-P02 Sample Questions

  1. Question 1

    Beginner

    Snowflake for Gen AI Overview · Snowflake Cortex AI overview and the Data Cloud approach to Gen AI

    A financial services company is evaluating Snowflake Cortex AI against external LLM providers for processing highly sensitive customer transaction summaries. The Chief Information Security Officer (CISO) mandates that no customer data can leave the organization's governed security perimeter. Which characteristic of Snowflake Cortex AI natively satisfies this compliance requirement?

    Answer and explanation

    Correct answer: B

    Snowflake Cortex AI provides fully managed, serverless LLM inference directly within the Snowflake Data Cloud. Because the models run inside Snowflake's secure perimeter, customer data is never transmitted to external third-party APIs, ensuring strict data-residency and compliance requirements are met natively.

  2. Question 2

    Intermediate

    Snowflake for Gen AI Overview · Gen AI use cases on Snowflake

    A data engineering team wants to orchestrate a complex agentic workflow where an AI agent can autonomously query sales data, execute Python scripts, and summarize findings. Which Snowflake feature is specifically designed to orchestrate and manage these agentic tools and workflows?

    Answer and explanation

    Correct answer: D

    Snowflake Intelligence is a platform designed to create and manage agentic AI workflows. It allows developers to build agents that can reason, use tools (like SQL execution or Python scripts), and orchestrate multi-step tasks across enterprise data.

  3. Question 3

    IntermediateMultiple answers

    Snowflake for Gen AI Overview · Snowpark Container Services and Snowflake Model Registry

    A healthcare organization requires a highly customized, open-source medical LLM (e.g., MedLlama) to process patient records. They cannot use managed Cortex functions due to the need for custom model weights. Which TWO Snowflake features must be utilized together to host and serve this custom open-source model securely? (Select TWO)

    Answer and explanation

    Correct answers: C, D

    To deploy custom or open-source models with specific weights, organizations use SPCS (which supports GPU compute pools) to host the model securely, and the Snowflake Model Registry to manage, version, and deploy the model artifacts.

    flowchart LR MR[Model Registry] -->|Deploy| SPCS[Snowpark Container Services] SPCS -->|Inference| DB[(Snowflake Data)]

  4. Question 4

    Beginner

    Snowflake for Gen AI Overview · Snowpark Container Services and Snowflake Model Registry

    True or False: Snowpark Container Services requires organizations to provision external AWS or Azure GPU virtual machines manually before deploying open-source Large Language Models in Snowflake.

    Answer and explanation

    Correct answer: B

    False. Snowpark Container Services provides fully managed compute pools, including GPU-backed instances, directly within Snowflake. Customers do not need to manually provision or manage underlying cloud provider VMs; they simply create a Snowflake compute pool specifying the desired instance family.

  5. Question 5

    Advanced

    Snowflake for Gen AI Overview · Snowflake Cortex AI overview and the Data Cloud approach to Gen AI

    A retail enterprise is architecting a new application to categorize millions of customer reviews into positive, neutral, or negative sentiments. The engineering team is debating between using the fully managed SNOWFLAKE.CORTEX.SENTIMENT function versus fine-tuning an open-source model and deploying it via Snowpark Container Services (SPCS).

    They have strict requirements to minimize infrastructure maintenance overhead and want the pricing to scale exactly with the number of tokens processed, without paying for idle compute time.

    Which architectural approach BEST meets these specific constraints?

    Answer and explanation

    Correct answer: D

    Cortex LLM functions are fully managed and serverless. They abstract away infrastructure management and bill based on the actual tokens processed. Deploying a custom model via SPCS requires provisioning compute pools, which incur costs as long as they are running (idle time is billed), and introduces infrastructure maintenance overhead.

  6. Question 6

    Intermediate

    Snowflake for Gen AI Overview · Gen AI use cases on Snowflake

    A developer wants to accelerate their SQL and Python development within the Snowflake UI using an AI assistant that provides auto-completion, code generation, and debugging help. Which Snowflake capability is designed specifically for this use case?

    Answer and explanation

    Correct answer: A

    Cortex Code is Snowflake's AI-powered development assistant that helps developers write SQL and Python faster by offering code generation, auto-completion, and debugging capabilities directly within Snowflake interfaces.

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