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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) The
B) 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.
C) Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
D) 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

E) 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.

2. A financial services company is developing an automated data pipeline in Snowflake to process Federal Reserve Meeting Minutes, which are initially loaded as PDF documents. The pipeline needs to extract specific entities like the FED's stance on interest rates ('hawkish', 'dovish', or 'neutral') and the reasoning behind it, storing these as structured JSON objects within a Snowflake table. The goal is to ensure the output is always a valid JSON object with predefined keys. Which AI_COMPLETE configuration, used within an in-line SQL statement in a task, is most effective for achieving this structured extraction directly in the pipeline?

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

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

B) The

C) Using the

D) For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
E) The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the

4. A development team is evaluating Snowpark Container Services (SPCS) for deploying various AI/ML workloads, including custom LLMs and GPU-accelerated model training. They need to understand its core benefits and operational characteristics compared to traditional container orchestration platforms. Which of the following statements accurately describe the benefits and/or operational characteristics of Snowpark Container Services for deploying third-party models and AI applications?

A) SPCS primarily supports applications written in Python and Java, with limited experimental support for other programming languages.
B) Compute clusters within SPCS are designed to auto-scale dynamically based on workload demand, automatically adjusting the number of instances.
C) Snowpark Container Services ensures that data remains within Snowflake's security and governance boundaries, eliminating the need to move data out of the environment for processing.
D) SPCS supports both long-running services (e.g., web applications) and finite-lifespan job services (e.g., GPU-accelerated machine learning model training).
E) SPCS provides a fully managed OCI runtime execution environment, allowing users to run containerized workloads directly within Snowflake without managing underlying Docker or Kubernetes infrastructure.

5. A data engineer is designing an automated pipeline to process customer feedback comments from a 'new_customer_reviews' table, which includes a 'review_text' column. The pipeline needs to classify each comment into one of three predefined categories: 'positive', 'negative', or 'neutral', and store the classification label in a new 'sentiment_label' column.
Which of the following statements correctly describe aspects of implementing this data transformation using 'SNOWFLAKE.CORTEX.CLASSIFY_TEXT' in a Snowflake pipeline?

A) The classification can be achieved by integrating a 'SELECT statement with

B) Including an optional 'task_description' such as

C) Both the input string to classify and the are case-sensitive, potentially yielding different results for variations in capitalization.
D) The argument must contain exactly three unique categories for sentiment classification.
E) The cost for 'CLASSIFY _ TEXT is incurred based on the number of pages processed in the input document.

Solutions: