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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. An ML engineer is deploying a custom PyTorch-based image classification model, obtained from Hugging Face, to Snowpark Container Services (SPCS). The deployment requires GPU acceleration on a compute pool named 'my_gpu_pool' and specific Python packages ('torch' , 'transformers' , 'opencv-python'). The scenario dictates that 'opencv-python' is only available via PyPl, while 'torch' and 'transformers' can be sourced from either conda-forge or PyPl. The engineer uses the Snowflake Model Registry to log the model. Which of the following and configurations correctly specify the necessary Python dependencies and GPU utilization for this inference service, adhering to Snowflake's recommendations?

A)

B)

C)

D)

E)

2. A data scientist is tasked with improving the accuracy of an LLM-powered chatbot that answers user questions based on internal company documents stored in Snowflake. They decide to implement a Retrieval Augmented Generation (RAG) architecture using Snowflake Cortex Search. Which of the following statements correctly describe the features and considerations when leveraging Snowflake Cortex Search for this RAG application?

A) Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
B) For optimal search results with Cortex Search, source text should be pre-split into chunks of no more than 512 tokens, even when using models with larger context windows like

C) The
D) Enabling change tracking on the source table for the Cortex Search Service is optional; the service will still refresh automatically even if change tracking is disabled.
E) To create a Cortex Search Service, one must explicitly specify an embedding model and manually manage its underlying infrastructure, similar to deploying a custom model via Snowpark Container Services.

3. A data engineering team wants to deploy a proprietary PyCaret classification model, saved as pycaret_best_model.pkl, for inference within Snowpark Container Services (SPCS). They need to register this custom model in the Snowflake Model Registry. Which of the following is a correct and essential step in this process?

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

4. A financial analytics team is developing an application to extract specific, structured financial data (e.g., company name, revenue, profit margin) from various news articles using Snowflake Cortex LLM functions. They require the output to strictly conform to a predefined JSON schema and want to ensure robust error handling. Which of the following statements are crucial considerations for achieving this goal?

A) For enhanced reliability in production pipelines, the team should wrap their AI_COMPLETE calls within TRY_COMPLETE, as it returns a structured error object if the model fails to adhere to the schema, allowing for detailed debugging.
B) The AI_COMPLETE function should be used with the response_format argument, supplying a JSON schema object that defines the required structure, data types, and constraints for the output.
C) The complexity of the JSON schema provided to AI_COMPLETE has no impact on compute costs, as only the input text and generated content tokens are billed.
D) To guarantee that critical fields like 'company name' and 'revenue' are always extracted, these properties must be explicitly listed within the ' required' array of the JSON schema provided to AI_COMPLETE.
E) Setting the temperature option to 0 in the AI_COMPLETE call is essential for obtaining the most consistent and accurate structured JSON outputs, regardless of task complexity or model used.

5. A data engineer is building a Snowflake data pipeline to ingest customer reviews from a raw staging table into a processed table. For each review, they need to determine the overall sentiment (positive, neutral, negative) and store this as a distinct column. The pipeline is implemented using SQL with streams and tasks to process new dat a. Which Snowflake Cortex LLM function, when integrated into the SQL task, is best suited for this sentiment classification and ensures a structured, single-label output for each review?

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

Solutions: