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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A machine learning engineer needs to fine-tune the 'mistral-7b' LLM using Snowflake Cortex for a specialized task. They have prepared training data in a Snowflake table. Which of the following statements correctly describe the process, requirements, and cost considerations for initiating this fine-tuning job?

A) The 'max_epochs' option in the 'options' JSON object can be set to any positive integer to control the training duration, but it is capped at a maximum of 50 epochs.
B) The cost for fine-tuning is incurred based on the number of tokens used in training, calculated as:

C) The fine-tuned model will automatically appear in the Snowflake Model Registry and is available for sharing with other accounts using Data Sharing, even if it contains user code.
D) To create the fine-tuning job, the engineer should use the SQL command:

E) The training data query result must explicitly include columns named

2. 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) 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.
B) 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.
C) 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.
D) 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.
E) 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.

3. A data engineering team is designing a Snowflake data pipeline to automatically enrich a 'customer issues' table with product names extracted from raw text-based 'issue_description' columns. They want to use a Snowflake Cortex function for this extraction and integrate it into a stream and task-based pipeline. Given the 'customer_issues' table with an 'issue_id' and (VARCHAR), which of the following SQL snippets correctly demonstrates the use of a Snowflake Cortex function for this data enrichment within a task, assuming is a stream on the 'customer issues' table?

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

4. An organization relies on Snowflake Cortex LLM functions and has established a robust model governance policy using the 'CORTEX MODELS_ALLOWLIST parameter. A developer is integrating 'TRY COMPLETE into an application for processing various text inputs. Which of the following statements are correct regarding 'TRY COMPLETE and model access controls?

A) If 'TRY_COMPLETE fails to perform its operation and returns ' NULL ' , the tokens that would have been processed for that specific call are not billed, ensuring cost efficiency for failed operations.
B) if a If a model specified in 'TRY_COMPLETE is not allowed by the and no RBAC grant exists for it, the 'TRY_COMPLETE function will automatically return 'NULL' to prevent a hard error.
C) The parameter primarily governs access for models invoked by their plain names with but 'TRY COMPLETE can still access models if the user has been granted appropriate RBAC on a fully-qualified model object.
D) The 'TRY_COMPLETE function can return a 'NULL ' value if the underlying LLM experiences a temporary internal processing error during response generation, allowing the query execution to continue without halting.
E) To provide a stateful conversational experience with 'TRY_COMPLETE, the function automatically retains context from previous calls within the same session.

5. A data operations team is attempting to scale up their Document AI automated pipeline. They are using a Snowflake Task to process a large volume of daily scanned invoices and receipts, which are stored in an internal stage 'financial_docs stage'. The current processing involves documents that are frequently around 75 MB each, and often there are batches exceeding 1 ,200 documents in a single day. The pipeline is failing consistently. Which of the following factors could be contributing to the failures in this Document AI automated pipeline? (Select all that apply.)

A) The account role executing the task lacks the 'SNOWFLAKCORTEX USER database role, which is a prerequisite for Document AI functions.
B) The was created without specifying 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.
C) The individual document size (75 MB) exceeds the maximum supported file size for Document AI.
D) The Snowflake Task is configured as a 'SERVERLESS TASK, which is not supported by Document AI.
E) The number of documents in a single daily batch (exceeding 1 ,200) surpasses Document AI's processing limit per query.

Solutions: