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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

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

2. An ML engineering team is preparing to log a custom Python model to the Snowflake Model Registry. This model has several Python package dependencies. The team wants to ensure the model can be deployed optimally, either in a Snowflake warehouse or to Snowpark Container Services (SPCS), depending on future needs. They are particularly concerned with how dependency specification impacts deployment eligibility. Which statements accurately describe how Snowflake handles model dependencies and determines deployment eligibility for custom Python models logged in the Model Registry, particularly when considering both Snowflake warehouse and Snowpark Container Services (SPCS) environments? (Select all that apply.)

A) The function will fail if 'WAREHOUSE is specified in 'target_platforms' but the model's size or GPU requirements make it ineligible for warehouse deployment.
B) Specifying both 'conda_dependencies' and 'pip_requirements' for a model is recommended to cover all possible deployment scenarios, and Snowflake's build process ensures compatibility between them.
C) When 'conda_dependencies' are specified for a model to be deployed to SPCS, these dependencies are by default obtained from 'conda-forge' rather than the Snowflake conda channel.
D) For models intended for SPCS, 'pip_requirements' are always preferred over 'conda_dependencies' because SPCS strictly prohibits the use of any conda packages from 'conda-forge'
E) If all of a model's 'conda_dependencieS are available in the Snowflake conda channel, the model is automatically deemed eligible to run in a warehouse.

3. A data analytics team is building a Retrieval Augmented Generation (RAG) application to provide contextual answers from a vast repository of internal documents stored in Snowflake. They are evaluating different strategies for generating and retrieving text embeddings to optimize the overall RAG pipeline's performance and relevance. Which of the following statements accurately describe performance considerations related to embedding generation and retrieval in this RAG context? (Select all that apply)

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

4. A Snowflake account administrator in an Azure East US 2 region needs to enable users to access a new, highly capable LLM, 'claude-3-5-sonnet' , which is currently only natively available in AWS regions via Snowflake Cortex. The administrator also wants to ensure that only specific, approved LLMs can be used across the organization. Which configuration steps are necessary for the administrator to achieve these requirements?

A) The 'CORTEX ENABLED CROSS REGION' parameter allows access to models in other regions, but access to specific LLMs is controlled solely by individual user privileges granted directly on the model objects, not by an account-level allowlist.
B) Since 'claude-3-5-sonnet' is an OpenAl model, the administrator must enable the 'ENABLE_CORTEX ANALYST MODEL AZURE OPENAI' account parameter, and then the model will automatically be available for cross-region inference without further action.
C) Create a 'COMPUTE POOL' with a 'GPU NV_S instance family in Azure East US 2, and then deploy a custom PyCaret model of 'claude-3-5-sonnet' to this pool through the Snowflake Model Registry.
D) Grant the 'SNOWFLAKE.CORTEX_USER database role to the relevant user roles. Set the account parameter to 'ANY REGION' or a list including an AWS region where 'claude-3-5-sonnet' is natively available. Additionally, configure the 'CORTEX MODELS ALLOWLIST to explicitly permit 'claude-3-5-sonnet' and other desired models.
E) Set the account parameter to include 'claude-3-5-sonnet' , and then set the account parameter to 'TRUE to allow cross-region inference for all Cortex features.

5. 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) Redefine extraction questions to be more generic and encompassing, reducing the number of distinct questions needed per document.
B) Implement a pre-processing step to split documents exceeding 125 pages or 50 MB into smaller, compliant files before loading to the stage.
C) 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.
D) Configure the internal stage used for storing invoices with 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.
E) Increase the 'max_tokens' parameter within the '!PREDICT' function options to accommodate longer document responses from the model.

Solutions: