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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A financial institution is fine-tuning a llama3.1-70b model within Snowflake Cortex using sensitive internal financial reports to improve sentiment analysis on earnings call transcripts. They need to understand the implications for data privacy, model ownership, and how this fine-tuned model can be managed and shared. Which of the following statements are true regarding this process?

A) The fine-tuning process requires the explicit provisioning and management of a Snowpark-optimized warehouse with GPU resources by the institution.
B) The resulting fine-tuned model (e.g., my_sentiment_model) is the exclusive property of the financial institution and cannot be accessed or used by any other Snowflake customer.
C) The financial reports used for fine-tuning the llama3.1-7b model are securely isolated and are not used by Snowflake to train or re-train models for other customers.
D) The fine-tuned model, being of type CORTEX_FINETUNED, can be shared with other Snowflake accounts using secure data sharing capabilities.
E) Fine-tuned LLMs built with Cortex Fine-tuning are fully managed through the Snowflake Model Registry API, allowing for programmatic deployment, version control, and comprehensive lifecycle management.

2. 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) To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the
B) For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
C) The

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

3. 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) 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.
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) 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.
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.

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

5. A Gen AI specialist is designing a RAG pipeline utilizing Cortex Search for an application that queries a large repository of unstructured text documents. To optimize the quality of retrieval and subsequent LLM responses, what are the critical best practices and understanding of Cortex Search's mechanisms that the specialist should consider regarding text processing and tokenization?

A) For best search results, text in the search column should be split into chunks of no more than 512 tokens, as smaller chunks generally lead to more precise retrieval and relevant LLM context.
B) Embedding models with larger context windows, such as snowflake-arctic-embed-1-v2.e-8k (8000 tokens), are always superior as they allow the RAG system to process the entire document as a single, highly relevant chunk.
C) Cortex Search operates solely on vector embeddings for semantic search; keyword-based retrieval is handled by a separate, less efficient mechanism outside the core search service.
D) When text input exceeds an embedding model's context window, Cortex Search truncates the text for both semantic embedding and keyword-based retrieval, potentially losing critical information.
E) The SNOWFLAKE .CORTEX. COUNT TOKENS function is a helper function that can be used to accurately determine the token count for a given string based on a specified model, aiding in adherence to context window limits.

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