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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q247-Q252):

NEW QUESTION # 247
A team is developing a critical business intelligence application that leverages Snowflake Cortex Analyst to provide natural language querying capabilities over complex structured dat a. To minimize operational costs while maintaining high accuracy, which of the following strategies are most effective for optimizing the cost efficiency of the Cortex Analyst service?

Answer: C,D

Explanation:
Option B is correct because a Verified Query Repository (VQR) helps Cortex Analyst leverage pre-validated SQL for similar questions, improving accuracy and potentially reducing the number of LLM inference calls or complex reasoning steps required for SQL generation, thus making usage more efficient and reducing cost associated with less optimal LLM calls. Option D is correct because integrating Cortex Search Services improves literal search, helping Cortex Analyst find exact literal values needed for SQL queries more accurately and efficiently, which can reduce ambiguity and the need for multiple LLM iterations or incorrect queries, ultimately leading to more cost-effective message processing. Option A is incorrect: While using a smaller LLM might seem to save cost, Llama 3.1 70B was specifically chosen as the summarization agent for multi-turn conversations in Cortex Analyst due to its higher accuracy in rephrasing questions and avoiding errors, implying that a less capable model would degrade performance and potentially lead to more (and thus more expensive) overall messages to achieve a correct answer. The cost for Cortex Analyst is per message, not per token for this component. Option C is incorrect. While a well- scoped semantic model is recommended for accuracy, the sources do not explicitly state that reducing the number of logical tables and columns 'directly' reduces the per-message cost of Cortex Analyst, which is fixed per message. The impact would be indirect through improved accuracy or reduced processing complexity, but not a direct cost reduction based on metadata size for the fixed per-message billing. Option E is incorrect. Cortex Analyst cost is based on the number of messages, not the token count of prompts. While good prompt engineering (like concise custom instructions) is generally good practice, it does not directly reduce the per-message cost of Cortex Analyst as it would for token- based LLM calls.


NEW QUESTION # 248

Answer: A,B,C,D,E

Explanation:


NEW QUESTION # 249
An operations manager is concerned about the governance and monitoring of a newly deployed Cortex Analyst application, especially regarding cost tracking and how multi-turn conversations are handled. Which statement accurately describes a feature for monitoring or managing a Cortex Analyst application?

Answer: A

Explanation:
Option C is correct. Cortex Analyst logs requests to an event table, which administrators can query using the function. This function requires specifying the semantic model type as either ''FILE ON STAGE'' for YAML files or ''SEMANTIC VIEW" for native semantic views, along with the model or view name, to retrieve details like the user, question, generated SQL, and errors. Option A is incorrect. Credit usage for Cortex Analyst is based on the number of messages processed (67 Credits per 1,000 messages), not the number of tokens in each message. Option B is incorrect. While Cortex Analyst supports multi-turn conversations by passing conversation history, it does not inherently cache previous responses. The source suggests resetting a long conversation if interpretation struggles, but not with a 'RESET CONVERSATION' command for cost optimization. Option D is incorrect. Snowflake strongly discourages using Azure OpenAI models via this legacy path, and specifically notes that if enabled, Azure OpenAI models do not respect RBAC restrictions, and metadata/prompts leave Snowflake's governance boundary. Option E is incorrect. Cortex Analyst functions (including 'COMPLETE) do not retain state between calls. To provide a stateful conversational experience, all previous user prompts and model responses must be explicitly passed as part of the array.


NEW QUESTION # 250
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: A

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 # 251
A business user frequently asks Cortex Analyst questions that require filtering on specific product names, such as "What were the sales for 'iced tea' last month?" The 'product' dimension has many distinct values (high cardinality), and Cortex Analyst sometimes struggles to accurately identify the exact literal product name, leading to less precise SQL queries. The Gen AI Specialist wants to enhance Cortex Analyst's ability to find these literal values for the 'product' dimension. To improve Cortex Analyst's literal search capability for the high-cardinality 'product' dimension, which of the following is the most appropriate and recommended approach to configure in the semantic model?

Answer: A

Explanation:
Cortex Analyst offers solutions to improve literal usage, including semantic search over sample values in the semantic model and semantic search using Cortex Search Services. For dimensions with high cardinality (many distinct values), creating a Cortex Search Service on the underlying column and specifying it in the field of the dimension within the semantic model is the recommended approach. This allows for high-quality "fuzzy" search to find literal values needed for Cortex Analyst's SQL queries. Option A is less effective for high-cardinality dimensions because only a fixed-size set of sample values is presented to the LLM, regardless of how many are provided. Option C is not the intended use for the 'description' field and could exceed context window limits. Option D, while a possible technical solution, bypasses the integrated and optimized Cortex Search functionality designed for this purpose. Option E is explicitly contradicted by the scenario, which indicates the LLM struggles, and the available solutions are designed to address this limitation.


NEW QUESTION # 252
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