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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q141-Q146):

NEW QUESTION # 141
An operations team is investigating an issue with a generative AI application powered by Snowflake Cortex Analyst, where users reported unexpected behavior in generated SQL. To diagnose the problem, they examine the detailed event logs captured by Snowflake AI Observability. Which categories of information can they expect to find in these event tables to assist their investigation?

A. The complete request and response bodies associated with the application's execution steps.
B. The exact SQL queries that Cortex Analyst generated in response to user questions.
C. Any error messages or warnings that occurred during the processing of the request.
D. The full text of the natural language questions submitted by the users.
E. Real-time CPU and memory usage statistics for the Snowflake virtual warehouse executing the LLM inference.

Answer: A,B,C,D

Explanation:
Cortex Analyst logs requests to an event table to aid in refining semantic models or views. These logs are comprehensive and include specific details crucial for debugging and monitoring. The captured information includes 'The user who asked the question', 'The question asked', 'Generated SQL', 'Errors and/or warnings', 'Request and response bodies', and 'Other metadata'. Therefore, options A, B, C, and D are all accurate descriptions of the data available in these event logs. Option E, real-time CPU and memory usage, refers to infrastructure monitoring metrics rather than the content specifically logged within the application's execution event table by Cortex Analyst itself.

NEW QUESTION # 142
An ML engineer is designing a Cortex Agent to provide highly accurate and contextualized responses. They intend for the agent to use state-of-the-art LLMs for orchestration and to maintain a specific brand tone in its outputs. Considering the available models and configurations for Cortex Agents, which statement is true?

A. When an agent utilizes an LLM for orchestration, the system ensures that cross-region inference is automatically enabled without any latency implications, making region selection irrelevant.
B. To ensure the agent's responses adhere to a desired brand and tone, 'Response instructions' can be configured, which guide the agent's output style and persona.
C. Cortex Agents primarily interact with data through the Snowflake Model Registry API to retrieve and update fine-tuned model parameters during their iterative planning phase.
D. The agent's 'Planning' component is specifically responsible for evaluating results after each tool use and deciding the subsequent steps in the query resolution process, acting as a feedback loop.
E. Cortex Agents are restricted to using only Snowflake Arctic models for orchestration, due to security and governance requirements, and must operate in the account's default region.

Answer: B

Explanation:
Option A is incorrect. Cortex Agents can use a variety of models for orchestration, including

,and others. Cross-region inference can be used if a model is not available in the local region. Option B is correct. 'Response instructions' allow configuration of the agent's responses to a preferred brand and tone. Option C is incorrect. While cross-region inference can be used for models not available locally, latency between regions depends on cloud provider infrastructure and network status. Therefore, it does have latency implications. Option D is incorrect. Cortex Agents leverage Cortex Analyst and Cortex Search as tools, along with LLMs, to analyze data. While fine-tuned models might be involved, the primary interaction is not described as being through the Snowflake Model Registry API for active data processing or parameter updates during the planning phase. The Model Registry is for logging and managing models. Option E is incorrect. The 'Reflection' component of Cortex Agents is responsible for evaluating results after each tool use to determine the next steps (asking for clarification, iterating, or generating a final response). The 'Planning' component is for orchestrating a plan to address a request.

NEW QUESTION # 143

Which of the following is a 'direct cause' of this error related to missing schema-level privileges, assuming the model build name is unique and the warehouse is active and accessible?

Answer: B

Explanation:

NEW QUESTION # 144
A financial institution uses Snowflake to process an average of 5 million PDF documents (average 10 pages each) monthly for regulatory compliance using AI_PARSE_DOCUMENT in LAYOUT mode. They are monitoring costs and considering performance optimizations. Given this scenario, which of the following statements about cost and performance for AI_PARSE_DOCUMENT are true?

A. If a document is a HTML file, the billing is based on processing each chunk of 3,000 characters as a page.
B. The primary billing metric for in LAYOUT mode is the number of tokens processed, which includes both input and output tokens.
C. Selecting a 'LARGE' or 'X-LARGE' standard warehouse will significantly improve the processing speed and reduce the per-page cost for AI_PARSE_DOCUMENT.
D. To reduce costs, the team should aim to process documents in batches, ensuring each query processes a maximum of 1 ,000 documents to avoid errors.
E. For optimal results with multilingual documents, the LAYOUT mode supports a broader range of languages than OCR mode.

Answer: A,D,E

Explanation:
Option A is incorrect. For 'AI_PARSE_DOCUMENT , billing is based on the number of document pages processed, not tokens. Specifically, LAYOUT mode costs 3.33 Credits per 1 ,000 pages. Option B is correct. Document AI supports processing a maximum of 1 ,000 documents in one query. Processing documents in batches up to this limit is a recommended practice to avoid errors and optimize processing. Option C is incorrect. Snowflake recommends executing queries that call with a smaller warehouse (no larger than MEDIUM) because larger warehouses do not increase performance. Option D is correct. LAYOUT mode for supports 12 languages (English, French, German, Hindi, Italian, Portuguese, Romanian, Russian, Spanish, Turkish, Ukrainian), while OCR mode supports 10 languages (English, French, German, Italian, Norwegian, Polish, Portuguese, Spanish, Swedish, Chinese), thus mode supports a broader LAYOUT range of languages. Option E is correct. For HTML and TXT files, each chunk of 3,000 characters is billed as a page, including the last chunk, which may be less than 3,000 characters.

NEW QUESTION # 145
A development team is constructing a Gen AI application using Snowflake Cortex LLM functions, particularly for conversational and text generation tasks. They are concerned about potential high costs due to token consumption. Which of the following strategies would most effectively help minimize token usage and optimize costs when working with these Cortex LLM functions?

A. To encourage more succinct LLM responses and reduce completion_tokens, configure the temperature option to a higher value (e.g., 0.7) in COMPLETE function calls.
B. In multi-turn conversations within Cortex Analyst, integrate a dedicated LLM summarization agent to rephrase follow-up questions, which reduces the total conversational history passed as context to the main LLM.
C. Utilize the COUNT_TOKENS (SNOWFLAKE .CORTEX) helper function to pre-validate the prompt length against the model's context window, thereby preventing truncation errors and subsequent re-runs.
D. For multi-turn conversational experiences using SNOWFLAKE .CORTEX.COMPLETE, only send the most recent user prompt in each API call, as the model automatically retains previous context.
E. When employing AI_COMPLETE for structured output tasks, providing concise and highly descriptive explanations for each field within the JSON schema will reduce the input tokens required for the LLM to understand and adhere to the schema accurately.

Answer: B,C,E

Explanation:
Option B is correct because while schema verification itself doesn't incur extra cost, a large or complex schema can increase token consumption. Providing precise and concise descriptions for schema fields helps the LLM understand and adhere to the desired format more efficiently, potentially reducing the overall tokens consumed for accurate responses. Option C is correct as the "COUNT _ TOKENS' function allows developers to determine the token count of an input prompt for a specific model, enabling them to pre-emptively avoid exceeding the model's context window, thus preventing errors and wasted compute from re-runs. Option E is correct because for multi-turn conversations in Cortex Analyst, a summarization agent is specifically used to rephrase follow-up questions by incorporating previous context, without passing the entire, potentially long, conversation history. This significantly reduces the 'prompt_tokens' sent to the main LLM for each turn and optimizes inference times. Option A is incorrect because 'COMPLETE' (and 'TRY_COMPLETE) functions are stateless; to maintain conversational context, all previous user prompts and model responses must be included in the array, which increases token count proportionally. Simply sending the latest prompt would lose context. Option D is incorrect as setting a higher 'temperature' value (e.g., 0.7) increases the 'randomness and diversity' of the LLM's output, not necessarily its conciseness for cost optimization. For the most consistent (and often direct) results, a 'temperature' of 0 is recommended.

NEW QUESTION # 146
......

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