Snowflake GES-C01

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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineer is tasked with establishing AI Observability for a generative AI application that integrates with external systems and will undergo continuous improvement. The goal is to compare different iterations of the application efficiently. Which combination of configuration best practices, features, and governance aspects are most relevant for a robust setup of AI Observability within Snowflake for this scenario?

A) Run the AI Observability project directly within a Snowflake Notebook to leverage its integrated environment for easier debugging and iteration.
B) If the AI Observability service is not natively available in the primary region, enable to ' ANY_REGION' or a specific supported region to allow tracing and evaluation to proceed.
C) To compare different LLMs or prompt configurations, rely on the AI Observability's 'Comparisons' feature, which allows side-by-side analysis of evaluation metrics across multiple evaluations.
D) For access control, the role used to create and execute runs must be granted the 'SNOWFLAKE.CORTEX_USER database role and the 'AI_OBSERVABILITY_EVENTS_LOOKUPS application role.
E) Ensure the Python environment includes 'trulens-core', 'trulens-connectors-snowflake', and 'trulens-providers-cortex' (version 2.1.2 or later) and set the environment variable TRULENS_OTEL_TRACING to 1.

2. A development team is evaluating Snowpark Container Services (SPCS) for deploying various AI/ML workloads, including custom LLMs and GPU-accelerated model training. They need to understand its core benefits and operational characteristics compared to traditional container orchestration platforms. Which of the following statements accurately describe the benefits and/or operational characteristics of Snowpark Container Services for deploying third-party models and AI applications?

A) Snowpark Container Services ensures that data remains within Snowflake's security and governance boundaries, eliminating the need to move data out of the environment for processing.
B) Compute clusters within SPCS are designed to auto-scale dynamically based on workload demand, automatically adjusting the number of instances.
C) SPCS primarily supports applications written in Python and Java, with limited experimental support for other programming languages.
D) SPCS provides a fully managed OCI runtime execution environment, allowing users to run containerized workloads directly within Snowflake without managing underlying Docker or Kubernetes infrastructure.
E) SPCS supports both long-running services (e.g., web applications) and finite-lifespan job services (e.g., GPU-accelerated machine learning model training).

3. A data processing team is using Snowflake Document AI to extract data from incoming supplier invoices. They observe that many documents are failing to process, and successful extractions are taking longer than expected, leading to increased costs. Upon investigation, they find error messages such as

. Additionally, their 'X-LARGE virtual warehouse is constantly active, contributing to higher-than-anticipated bills. Which two of the following actions are essential steps to troubleshoot and address the root causes of these processing errors and optimize their Document AI pipeline?

A) Increase the 'max_tokens' parameter within the '!PREDICT' function options to accommodate longer document responses from the model.
B) Scale down the virtual warehouse to 'X-SMALL' or 'SMALL' size, as larger warehouses do not increase Document AI query processing speed and incur unnecessary costs.
C) Redefine extraction questions to be more generic and encompassing, reducing the number of distinct questions needed per document.
D) Implement a pre-processing step to split documents exceeding 125 pages or 50 MB into smaller, compliant files before loading to the stage.
E) Configure the internal stage used for storing invoices with 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.

4. A data analyst is using Snowflake Copilot in Snowsight to generate SQL queries for a new dataset containing customer PII. Which of the following statements accurately describes how Snowflake Copilot operates with respect to data access, governance, and model interaction?

A) Snowflake Copilot directly accesses and processes the raw data within customer tables to understand its content and generate SQL.
B) Snowflake Copilot requires explicit column-level grants for direct data access, similar to how a human analyst would query specific data points.
C) While Snowflake Copilot generates SQL based on metadata, the generated SQL queries are executed in an isolated environment that does not respect existing Snowflake RBAC policies.
D) To protect sensitive information, Snowflake Copilot transmits sampled PII data to an external LLM for schema understanding before generating SQL.
E) Snowflake Copilot is powered by a fine-tuned model that runs securely inside Snowflake Cortex, leveraging only database/schema/table/column names and data types, ensuring data remains within Snowflake's governance boundary and respects RBAC.

5. 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) 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.
C) For embedding text, selecting a model like

D) The
E) CHANGE_TRACKING

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