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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
B) Using the

C) The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the
D) To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the

E) The

2. A data engineer is designing an automated pipeline in Snowflake to process streaming customer support tickets using LLM functions. To ensure the pipeline is robust against unexpected LLM errors (e.g., due to malformed input or transient service issues), they decide to use SNOWFLAKE.CORTEX.TRY_COMPLETE instead of SNOWF LAKE .CORTEX.COMPLETE
. Which of the following is the most significant advantage of using
TRY_COMPLETE
in this scenario?

A) It processes tokens at a significantly reduced rate compared to
B) It offers enhanced security features, such as automatic input sanitization, which are not available in the standard
C) It provides a built-in retry mechanism for LLM calls that encounter temporary failures, automatically re-submitting the prompt up to three times.
D) It returns
E) It automatically adjusts the LLM's

3. A team is designing a complex Gen AI application in Snowflake, which includes components for training a custom LLM, running batch inference, and providing a real-time conversational interface. They plan to leverage Snowpark Container Services (SPCS) for these workloads. Which of the following statements accurately describe the suitable SPCS service design models and important considerations for these different application components? (Select all that apply.)

A) When deploying LLMs to SPCS, it's generally most cost-efficient to use generic CPU instance types like 'CPU X64 XS' for all tasks, as GPU instances (e.g., are exclusively for highly specialized computer vision tasks and not optimized for LLMs.
B) For batch inference on Snowflake data where data locality and efficiency are key, using ''Service Functions'' is highly efficient because data is passed as input parameters directly from SQL queries, and this design ensures the data never leaves the Snowflake network boundary.
C) Real-time LLM inference for a conversational interface is ideally deployed as a ' 'Service' ' in SPCS, which is long-running and accessible via an HTTP endpoint, ensuring continuous availability and responsiveness.
D) Container images for SPCS deployments are typically pushed to a public Docker Hub repository, and Snowflake pulls them as needed during service creation and scaling, simplifying image management.
E) GPU-accelerated LLM training, which is a finite and often resource-intensive task, is best implemented as a ''job'' in SPCS, invoked via "EXECUTE JOB SERVICE', as it is designed to run to completion and then spin down.

4. A data engineering manager needs to audit Cortex LLM function costs to identify specific SQL queries that are unexpectedly high in token consumption for the 'llama3.1-8b' model. They require granular analysis of prompt, completion, and guardrail token usage for these queries. Which of the following Snowflake methods or views would provide the necessary insights?

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

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 cost for 'CLASSIFY _ TEXT is incurred based on the number of pages processed in the input document.
B) The argument must contain exactly three unique categories for sentiment classification.
C) Including an optional 'task_description' such as

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

E) Both the input string to classify and the are case-sensitive, potentially yielding different results for variations in capitalization.

Solutions: