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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q122-Q127):
NEW QUESTION # 122
A data analyst needs to use SNOWFLAKE. CORTEX. EXTRACT_ANSWER to streamline information retrieval from various contract documents. They are new to Cortex functions and want to understand access requirements and optimal usage. Which of the following statements about using SNOWFLAKE .CORTEX. EXTRACT_ANSWER are correct?
Answer: A,B,C
Explanation:
Option A is correct because users must use a role that has been granted the 'SNOWFLAKE.CORTEX USER database role to access EXTRACT_ANSWER and other Cortex AI functions. Option B is correct because for optimal performance and accurate responses, it is recommended to use plain English text for input and categories, and questions should be specific and ask for a single value. Option C is incorrect; EXTRACT_ANSWER would typically raise an error if an operation cannot be performed. The 'TRY_COMPLETE function is specifically designed to return 'NULL' instead of an error in such cases. Option D is incorrect; 'EXTRACT ANSWER is an older version of this function, and 'AI_EXTRACT' is the latest version, which supports additional capabilities like image and multi-language extraction. Option E is correct; EXTRACT ANSWER can be called on a table column, enabling efficient batch processing of multiple documents or text entries. This is a common pattern for integrating Cortex functions into data pipelines.
NEW QUESTION # 123
A Gen AI Specialist in Snowflake Cortex aims to fine-tune an LLM for enhanced task-specific performance. When creating a fine-tuning job using SNOWFLAKE. CORTEX. FINETUNE( 'CREATE', ... ) , which statement accurately describes the required training data format and a supported base model, aligning with Snowflake's Gen AI principles for leveraging LLMs?
Answer: D
Explanation:
To create a fine-tuning job, the 'SNOWFLAKE.CORTEX.FINETUNE('CREATE', ...y function requires the training data query to return columns named 'prompt' and 'completion'. The 'mistral-7b& model is listed as a supported base model for fine-tuning. Models like 'llama4- maverick' , 'openai-gpt-4.1', 'snowflake-arctic' , and 'claude-4-opuS are not listed as base models available for fine-tuning using this function.
NEW QUESTION # 124
An organization operating in the AWS US West 2 (Oregon) region needs to process sensitive customer support tickets using Snowflake Cortex LLM functions. Due to the diverse availability of specific LLMs, they are considering enabling CORTEX_ENABLED_CROSS_REGION. What is a key data safety and security consideration when enabling CORTEX_ENABLED_CROSS_REGION for Snowflake Cortex LLM functions, specifically regarding data storage and persistence?
Answer: D
Explanation:
Option C is correct. The parameter enables inference requests to be processed in a different region from the default. A key data safety consideration is that user inputs, service generated prompts, and outputs are *not stored or cached* during cross-region inference. This ensures that while data is transmitted across Snowflake regions for processing, it does not persist in intermediate storage. Option A is incorrect because while cross-region inference might incur increased latency, the statement focuses on cost and implies data movement guarantees are unchanged, which is partially true but misses the specific non-storage aspect of the data safety consideration. Option B is incorrect as the sources explicitly state that user inputs and outputs are *not stored or cached* during cross-region inference. Option D is incorrect because controls *where* inference happens, not *which* models are allowed; the 'CORTEX MODELS ALLOWLIST parameter governs model access and is a separate control. Option E is incorrect because while Snowflake maintains robust data protection, the claim that encryption keys are managed by the *third-party cloud provider in the remote region* is not explicitly stated as a default behavior and Snowflake maintains control over data within its service boundary.
NEW QUESTION # 125
A data application developer is tasked with creating a multi-turn conversational AI application using Streamlit in Snowflake (SiS), which will leverage Snowflake Cortex LLM functions. Considering the core requirements for building such an interactive chat interface and the underlying Snowflake environment, which of the following actions is a fundamental step in setting up the application for stateful conversations?
Answer: D
Explanation:
For a multi-turn conversational AI application built with Streamlit, maintaining the conversation history is fundamental. Streamlit's st.session_state' is the primary way to store and manage state across reruns of the application, which is crucial for remembering past interactions in a chat interface. The typical approach involves initializing 'st.session_state.messages' to an empty list and appending messages for each turn. Option A is incorrect because is a database role specific to Document AI, not general Cortex LLM functions. Option C is not a fundamental step for running a Streamlit application in Snowflake (SiS) itself, as SiS directly hosts the Streamlit app; while models can be served via SPCS, the application itself doesn't inherently require it for basic operation. Option D is related to cross-region inference for LLM functions, which controls where inference requests are processed, not a fundamental step for local execution or conversational state management. Option E suggests a configuration ("ON ERROR':'SKIP") that is primarily used with Snowflake ML functions like Anomaly Detection and Time-Series Forecasting to prevent overall training failure for individual series, and is not a direct option for handling errors in 'TRY_COMPLETE in this manner; 'TRY_COMPLETE itself returns NULL on error.
NEW QUESTION # 126
A data platform administrator needs to retrieve a consolidated overview of credit consumption for all Snowflake Cortex AI functions (e.g., LLM functions, Document AI, Cortex Search) across their entire account for the past week. They are interested in the aggregated daily credit usage rather than specific token counts per query. Which Snowflake account usage views should the administrator primarily leverage to gather this information?
Answer: D
Explanation:
NEW QUESTION # 127
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