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Snowflake GES-C01 Exam Overview:

Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A data engineering team is implementing a solution using Snowflake Cortex's AI_COMPLETE function to process customer support tickets. They are concerned about sensitive information and ensuring the model's responses are safe, while adhering to Snowflake's data governance principles. Which of the following statements correctly describe the functionality of Cortex Guard and Snowflake's data privacy commitments in this context?

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

2. 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) The

B) Using the

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

3. A data engineer is tasked with establishing a robust MLOps pipeline using the Snowflake Model Registry. They have trained a scikit-learn model and need to log it. Which of the following statements correctly describes a 'required' step or privilege for successfully logging a model using the 'Registry.log_model' method?

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

4. A data engineering team needs to implement a highly accurate, low-latency solution for classifying specialized technical documents into 50 distinct categories. They are considering fine-tuning a Large Language Model (LLM) within Snowflake Cortex for this task. Which of the following considerations are critical for optimizing the fine-tuned model's performance and minimizing inference latency for production use? (Select all that apply)

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

5. A data engineer is integrating SNOWFLAKE. CORTEX. CLASSIFY_TEXT into an automated data pipeline that uses dynamic tables to process and transform streaming text dat a. They have ensured that the service account used has been granted the necessary SNOWFLAKE. CORTEX_USER database role. After deploying the pipeline, they consistently receive an error whenever CLASSIFY_TEXT is invoked. Which of the following is the most likely cause of the error encountered by the data engineer?

A) The input text being processed by 'CLASSIFY _ TEXT includes extensive non-plain English content, such as code blocks, which causes the function to fail with an error.
B) Snowflake Cortex functions, including 'CLASSIFY_TEXT , currently do not support integration with dynamic tables within data pipelines.
C) The array contains more than 100 unique categories, exceeding the maximum allowed limit for the function.
D) The 'task_description' provided in the optional arguments for 'CLASSIFY_TEXT exceeds the recommended length of approximately 50 words, leading to a validation error.
E) The role used by the data engineer, despite having 'SNOWFLAKE.CORTEX_USER, lacks the fundamental 'USAGE privilege on the database where the text data is stored.

Solutions: