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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q178-Q183):

NEW QUESTION # 178
A data team is refining their Cortex Analyst semantic model to improve the accuracy of responses for specific, frequently asked questions and to enable better literal value searches. Consider a semantic model being developed to address these requirements. Which two configurations or features are directly relevant and correctly applied in the semantic model YAML for these purposes?

Answer: C,D

Explanation:
Option A is correct. Cortex Search Services can be integrated into a dimension's definition (using the field with 'service' and fields) to improve literal matching by performing semantic search over the underlying column, which enhances Cortex Analyst's ability to find literal values for filtering. Option B is correct. The 'verified_querieS section allows pre-defining accurate SQL queries for specific natural language questions. Setting 'use_as_onboarding_question true' ensures these queries are used when relevant and presented as suggested questions to users. Option C is incorrect; while metrics can reference logical columns, 'relationships' between logical tables are necessary for defining joins, especially across different underlying base tables. Option D is incorrect; 'custom_instructions' are provided at the model level to give general context to the LLM for SQL query generation, not embedded within individual dimension definitions. Option E is incorrect; the 'sample_valueS field is recommended for dimensions with relatively low-cardinality (approximately 1-10 distinct values) to aid in semantic search for literals, not for high-cardinality dimensions.


NEW QUESTION # 179
A large e-commerce company plans to implement real-time sentiment analysis on millions of incoming customer reviews using SNOWFLAKE. CORTEX. SENTIMENT. They are concerned about managing costs and ensuring efficient processing. Which of the following statements about cost considerations and performance optimizations for SNOWFLAKE. CORTEX. SENTIMENT are true?
(Select all that apply)

Answer: A,C,D

Explanation:
Option B is correct. Snowflake recommends executing queries that call Cortex AI functions, including 'SENTIMENT , with a smaller warehouse (no larger than MEDIUM) because larger warehouses do not increase performance for these operations. Option C is correct. For functions like 'SENTIMENT, Snowflake adds an internal prompt to the input text in order to generate the response, which results in a higher input token count for billing than the raw text provided. Option E is correct. The 'Sentiment' function incurs compute cost at a rate of 0.08 Credits per one million Tokens. Option A is incorrect because for SENTIMENT , only input tokens are counted towards the billable total, not output tokens. Output tokens are typically billed for functions that generate new text, such as SAI COMPLETE or 'SUMMARIZE. Option D is incorrect because 'AI_SENTIMENT is an updated version of the function, but it still incurs compute costs based on tokens processed and is not free.


NEW QUESTION # 180
A data engineering team is tasked with improving the accuracy of a Cortex Analyst solution for a large e-commerce product catalog. Users frequently ask natural language questions involving specific product names, brands, and categories. The team observes that Cortex Analyst sometimes struggles to identify and correctly filter by these literal values in the generated SQL. Which of the following configurations or approaches, within the semantic model, can effectively enhance Cortex Analyst's ability to precisely identify and use literal values for filtering, based on Snowflake's best practices?

Answer: C,D

Explanation:
Options A and B are correct. For dimensions with low cardinality (around 1-10 distinct values), setting 'is_enum: true' and providing an exhaustive 'sample_values' list ensures Cortex Analyst chooses only from that predefined list, improving literal usage. For higher cardinality dimensions, integrating a Cortex Search Service via the entry, specifying both the 'service' name and the , allows semantic search over the underlying data to find appropriate literal values. Option C is incorrect because Cortex Analyst leverages semantic similarity search or Cortex Search for literal values, not direct 'LIKE clauses in the 'expr' field. Option D is incorrect because while 'verified_queries' improve accuracy for specific, known questions, they are not a scalable solution for all possible literal search scenarios and are not the primary mechanism for improving general literal value identification. Option E is incorrect because the 'max_tokens' parameter controls the length of the LLM's output response, not its ability to identify or filter by literal values.


NEW QUESTION # 181
A data engineering team is onboarding a new client whose workflow involves extracting critical financial data from thousands of daily scanned PDF receipts. They decide to use Snowflake Document AI and store all incoming PDFs in an internal stage name. After deploying their pipeline, they observe intermittent failures and varying error messages in the output, specifically:

Which two of the following actions are most likely required to resolve these processing errors?

Answer: B,D

Explanation:
The first error message, 'cannot identify image file', is a known error that occurs when an internal stage used for Document AI is not configured with 'SNOWFLAKE_SSE encryption. Therefore, option A is a direct solution. The second error message, 'Document has too many pages. Actual: 130. Maximum: 125.', indicates that some documents exceed Document AI's page limit of 125 pages per document. Option B directly addresses this limitation. Option C is incorrect because 'max_tokens' is relevant for LLM output length, not document input page/size limits. Option D is incorrect because scaling up the warehouse for Document AI does not increase query processing speed and is not recommended for cost efficiency; X-Small, Small, or Medium warehouses are typically sufficient for Document AI. Option E is incorrect because is the required database role for Document AI, not 'SNOWFLAKE.CORTEX_USER'.


NEW QUESTION # 182
A data engineering team is building a pipeline in Snowflake that uses a SQL task to call various Snowflake Cortex LLM functions (e.g., AI_COMPLETE, AI EMBED) on large datasets of customer interaction logs. The team observes fluctuating costs and occasional query failures, which sometimes halt the pipeline. To address these issues and ensure an efficient, robust, and monitorable pipeline, which of the following actions or considerations are essential? (Select all that apply.)

Answer: A,C,D

Explanation:
A). "Correct." The 'TRY function is designed to perform the same operation as but returns 'NULL ' instead of raising an error when the LLM operation cannot be performed. This is critical for building robust data pipelines, as it prevents pipeline halts due to transient or specific LLM failures, allowing for more resilient data processing. B. ' The view provides detailed information on token consumption and credit usage for Snowflake Cortex LLM functions. Monitoring this view is essential for understanding cost drivers and optimizing expenditure within AI pipelines. C. "Incorrect." Snowflake recommends executing queries that call Cortex AISQL functions with a smaller warehouse (no larger than MEDIUM), as larger warehouses do not necessarily increase performance but can lead to unnecessary costs. The LLM inference itself runs on Snowflake-managed compute, not solely on the user's virtual warehouse compute size. D. ' Setting the 'temperature' parameter to 0 makes the LLM's output more deterministic and focused. While this can be beneficial for consistency in certain tasks, it does not directly minimize token usage. Token usage is primarily determined by the length of the input prompt and the length of the generated output, which can vary regardless of 'temperature'. E. "Correct." Encapsulating complex and potentially lengthy prompt logic within a UDF CUSER DEFINED FUNCTION') makes the prompts more manageable, reusable, and easier to integrate programmatically into SQL statements within a data pipeline. This improves code organization and maintainability.


NEW QUESTION # 183
......

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