Snowflake GES-C01 Exam Dumps Collection & GES-C01 Vce Torrent
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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q164-Q169):
NEW QUESTION # 164
A Snowflake data engineering team needs to implement a system where they can call the SNOWFLAKE .CORTEX. COMPLETE function using the model, which is hosted in a different region than their primary Snowflake account. They also want to ensure that only llama3.1-8b approved models are callable. Which of the following configurations or privileges are necessary to achieve these requirements?
- A. Option C
- B. Option A
- C. Option E
- D. Option B
- E. Option D
Answer: A,B,D
Explanation:
NEW QUESTION # 165
A Snowflake administrator needs to configure Snowflake Copilot for a team distributed across different geographical regions, some of which are not natively supported for Copilot. Additionally, the team requires Copilot to adopt a specific tone in its responses. Which of the following correctly outlines the configuration steps for these requirements?
- A. Option E
- B. Option B
- C. Option A
- D. Option D
- E. Option C
Answer: B
Explanation:
NEW QUESTION # 166
A Gen AI Specialist is setting up AI Observability for a new generative AI application in Snowflake to monitor its performance and debug issues. The application is built in Python. Which of the following prerequisites must be met to enable tracing for this application?
- A. The Python project requires installation of trulens-core and trulens-connectors-snowflake packages, with versions earlier than 2.1.2.
- B. The role used for creating and executing runs must have the CREATE EXTERNAL AGENT privilege on the schema.
- C. The Python environment must have the TRULENS_OTEL_TRACING environment variable set to o before connecting to Snowflake.
- D. The application must run exclusively within a Snowflake Notebook for observability features to function.
- E. The account role used must be granted the SNOWFLAKE. CORTEX_USER database role.
Answer: B,E
Explanation:
NEW QUESTION # 167
A Gen AI developer is using 'SNOWFLAKE.CORTEX.COMPLETE" to generate concise summaries of legal documents. Initially, the LLM sometimes provides overly creative or slightly off-topic responses, indicating potential 'hallucinations' or a lack of focus. To improve the factual accuracy and conciseness of the summaries, which combination of prompt engineering techniques and 'COMPLETE' function options should be prioritized?
- A. Instruct the model to 'think out loud' with an inner monologue in the prompt, and set 'max_tokens' to a large value to allow full reasoning.
- B. Use a system prompt defining a persona like 'creative writer' and set 'temperature' to 0.9 to encourage diverse summaries.
- C. Rely solely on a comprehensive list of 'stop sequences' to end generation when the summary is complete.
- D. Implement 'first principles thinking' in the prompt, clearly outlining logical steps for summarization, and set 'temperature' to 0 for deterministic output.
- E. Provide a 'task description' focusing on broad themes and set 'top_p' to 0.5 to balance creativity and relevance.
Answer: D
Explanation:
To reduce 'hallucinations' and improve factual accuracy and conciseness, applying 'first principles thinking' by breaking prompts into logical steps helps the model respond from foundational concepts rather than assumptions. Setting 'temperature' to 0 yields the most consistent and deterministic results, which is crucial for factual accuracy and conciseness in summaries. Defining a 'creative writer' persona or using a high 'temperature' would increase diversity and potentially lead to more hallucinations (Option A). Instructing the model to 'think out loud' (Option C) enhances transparency of thought but does not directly enforce factual accuracy or conciseness in the way 'first principles' and low 'temperature' do. 'max_tokens' (Option C) affects output length, not necessarily accuracy or conciseness. A broad 'task description' (Option D) might not be specific enough, and 'top_p' also influences diversity. 'Stop sequences' (Option E) help with truncation but do not prevent factual errors or improve conciseness directly from the model's generation process.
NEW QUESTION # 168
A Gen AI specialist is preparing to upload a large volume of diverse documents to an internal stage for Document AI processing. The objective is to extract detailed information, including lists of items and potentially classifying document types, and then automate this process. Which of the following statements represent 'best practices or important considerations/limitations' when preparing documents and setting up the Document AI workflow in Snowflake? (Select ALL that apply.)
- A. For continuous processing of new documents, it is best practice to create a stream on the internal stage and a task to automate the '!PREDICT method execution.
- B. Documents with a page count exceeding 125 pages or a file size greater than 50 MB will be processed, but with a potential reduction in extraction accuracy.
- C. If the Document AI model does not find an answer for a specific field, the '!PREDICT method will omit the 'value' key but will still return a 'score' key to indicate confidence that the answer is not present.
- D. When defining data values for extraction, especially for nonstandard formats or combinations of values, fine-tuning the model with annotations is generally more effective than relying solely on complex prompt engineering.
- E. To improve model training, documents uploaded should represent a real use case, and the dataset should consist of diverse documents in terms of both layout and data.
Answer: A,C,D,E
Explanation:
NEW QUESTION # 169
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