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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A Streamlit application developer wants to use AI_COMPLETE (the latest version of COMPLETE (SNOWFLAKE.CORTEX)) to process customer feedback. The goal is to extract structured information, such as the customer's sentiment, product mentioned, and any specific issues, into a predictable JSON format for immediate database ingestion. Which configuration of the AI_COMPLETE function call is essential for achieving this structured output requirement?

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

2. A Gen AI Engineer is configuring a new semantic model for Cortex Analyst to process customer feedback. The goal is to ensure that when a user asks for sentiment analysis, the generated SQL queries always include an aggregation by a dimension and present the results as a percentage. The engineer plans to use custom instructions for this purpose. Which of the following details about is true and crucial for successful implementation?

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

3. 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 PyPI, while 'torch' and 'transformers' can be sourced from either conda-forge or PyPI. The engineer uses the Snowflake Model Registry to log the model. Which of the following 'log model' and 'create_service' configurations correctly specify the necessary Python dependencies and GPU utilization for this inference service, adhering to Snowflake's recommendations?

A)

B)

C)

D)

E)

4. A data engineer is designing an automated data pipeline in Snowflake to process incoming customer feedback documents. The pipeline needs to perform the following steps: 1. Extract the overall sentiment from the feedback text. 2. Generate a concise summary of each feedback document. 3. Extract key entities (e.g., product, issue, customer name) into a structured JSON format using a powerful LLM, ensuring adherence to a predefined schema and graceful error handling. Which of the following Snowflake Cortex features and best practices should the data engineer leverage to build this robust AI-infused pipeline?

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

5. An analytics team is preparing documents for a new Document AI model build to extract information from internal policy reviews. They have a variety of documents that they intend to upload to an internal stage for processing. The document list includes: (1 ) a 70 MB PDF with 100 pages, (2) a 45 MB DOCX with 150 pages, (3) a 30 MB PNG image, (4) a 60 MB TIFF image, and (5) a 20 MB HTML file. All documents are in English. Which of these documents would 'fail' to meet the direct input requirements for Document AI processing?

A) The 60 MB TIFF image.
B) The 30 MB PNG image.
C) The 45 MB DOCX with 150 pages.
D) The 70 MB PDF with 100 pages.
E) The 20 MB HTML file.

Solutions: