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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data application developer is building a Streamlit chat application that interacts with structured data using Cortex Analyst, a feature of Snowflake Cortex that is often integrated into Cortex Agents. The developer wants to ensure efficient multi-turn conversations and leverage verified queries. Which of the following statements represent best practices and functional aspects for this scenario within Snowflake's Gen AI ecosystem?

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

2. A team is building a critical Document AI pipeline for continuous processing of new financial reports. They've identified that occasionally, the 'GET_PRESIGNED_URL' generated for specific documents expires before the '!PREDICT function can successfully process them, resulting in HTTP 403 errors. To mitigate this, they plan to modify their SQL query logic.
Which approach effectively addresses the presigned URL expiration issue without altering the stage definition or the model build itself, and adheres to recommended practices for handling batch processing as described in Snowflake's troubleshooting documentation?

A)

B)

C)

D)

3. A data application developer is building a Streamlit chat application within Snowflake. This application uses a RAG pattern to answer user questions about a knowledge base, leveraging a Cortex Search Service for retrieval and an LLM for generating responses. The developer wants to ensure responses are relevant, concise, and structured. Which of the following practices are crucial when integrating Cortex Search with Snowflake Cortex LLM functions like AI_COMPLETE for this RAG chatbot?

A) The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the
B) The

C) To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the

D) Using the

E) For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single

4. A data science team is using SNOWFLAKE. CORTEX. CLASSIFY_TEXT to categorize product reviews into detailed segments like 'Bug Report - Critical', 'Feature Request - UI/UX', 'General Praise', or 'Query - Billing Issue'. For highly nuanced reviews, they find the initial classifications lack precision, and they are also concerned about the associated compute costs for processing large volumes of dat a. Which strategies should they employ to optimize classification accuracy and manage costs effectively with this function?

A) To reduce input token costs for classifications, the input text should be pre-processed to remove common stop words and punctuation, as these characters are counted as billable tokens without contributing to classification accuracy.
B) CLASSIFY_TEXT labels, descriptions, and examples are counted as input tokens only once per function call, regardless of the number of records processed in a batch, to optimize cost efficiency.
C) To improve accuracy for ambiguous classifications, they should augment the list_of_categories with explicit description and examples for each category, understanding that these additions will increase input token costs for each record processed.

D) For complex scenarios where the relationship between review text and categories is not straightforward, including a concise task_description (e.g., 'Classify the product review focusing on technical support relevance') in the options argument is recommended to guide the model.

E) If classifying thousands of reviews, they can significantly reduce overall compute costs by setting the temperature option to 0.0 within CLASSIFY_TEXT to ensure deterministic and cheaper inference.

5. 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) 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.
B) 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.
C) 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.
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) 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.

Solutions: