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TOP GES-C01 Latest Exam Test - Snowflake SnowPro® Specialty: Gen AI Certification Exam - Trustable GES-C01 Latest Exam Pdf

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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
A company is using Snowflake AI Observability to evaluate a summarization application. The application utilizes SNOWFLAKE. CORTEX. COMPLETE for LLM inference and a custom Python component for text pre-processing. The team is particularly interested in tracking detailed cost breakdowns and assessing the factual correctness of the LLM-generated summaries. Which of the following statements accurately describe the cost implications and evaluation metric capabilities in this scenario?

A. AI Observability incurs charges for the LLM judges invoked via COMPLETE (SNOWFLAKE .CORTEX) calls to compute evaluation metrics, in addition to warehouse charges for managing runs and queries.
B. To measure the factual correctness of LLM-generated summaries based on original input and avoid hallucinations, the 'factual correctness' and 'comprehensiveness' metrics can be used during evaluations.
C. The ORTEX_DOCUMENT_PROCESSING_USAGE_HISTORY view is the primary tool to monitor the credit consumption specifically for AI Observability evaluations and LLM judge usage.
D. The cost of AI Observability is primarily determined by the number of messages processed, and the number of tokens in each message does not affect the cost, ensuring predictable pricing.
E. For evaluating summarization tasks, the 'context relevance' score is the most important metric, as it directly assesses the quality of the LLM's output against the source document.

Answer: A,B

Explanation:

NEW QUESTION # 12
A team of data application developers is leveraging Snowflake Copilot to streamline the creation of analytical SQL queries within their Streamlit in Snowflake application. They observe that Copilot sometimes struggles with complex joins or provides suboptimal queries when dealing with a newly integrated, deeply nested dataset. Based on Snowflake's best practices and known limitations, which actions or considerations would help improve Copilot's performance in this scenario?

A. Break down complex requests into simpler, multi-turn questions, as Copilot is designed to build complex queries through conversational refinement and follow-up questions.
B. Ensure that a database and schema are explicitly selected for the current session, and that column names are meaningful, to provide Copilot with better context for query generation.
C. Implement curated views with descriptive and easy-to-understand names for views and columns, appropriate data types, and pre-define common/complex joins to simplify the underlying schema for Copilot.
D. Grant Copilot direct access to the raw data using ACCOUNTADMIN privileges, allowing it to infer schema relationships more effectively from data content.
E. Enable the CORTEX_MODELS_ALLOWLIST parameter to restrict Copilot to only use the largest available LLMs, thereby guaranteeing higher accuracy for complex queries.

Answer: A,B,C

Explanation:
To improve Snowflake Copilot's performance, creating curated views with descriptive names, appropriate data types, and capturing common/complex joins is a key best practice. Copilot can build complex queries through a conversation by asking follow-up questions. It also uses the names of databases, schemas, tables, and columns, and their data types to determine available data, so ensuring these are meaningful and correctly set for the session is crucial for relevant responses. Option B is incorrect because CORTEX_MODELS_ALLOWLIST controls access to specific LLMs but doesn't guarantee higher accuracy for Copilot's SQL generation. Option D is incorrect as Snowflake Copilot does not have access to the data inside tables; it operates on metadata. Granting privileges would not change this fundamental operational principle and is ACCOUNTADMIN against best practices for least privilege.

NEW QUESTION # 13
A financial services company is developing an automated data pipeline in Snowflake to process Federal Reserve Meeting Minutes, which are initially loaded as PDF documents. The pipeline needs to extract specific entities like the FED's stance on interest rates ('hawkish', 'dovish', or 'neutral') and the reasoning behind it, storing these as structured JSON objects within a Snowflake table. The goal is to ensure the output is always a valid JSON object with predefined keys. Which AI_COMPLETE configuration, used within an in-line SQL statement in a task, is most effective for achieving this structured extraction directly in the pipeline?

A. Option D
B. Option A
C. Option E
D. Option C
E. Option B

Answer: D

Explanation:
To ensure that LLM responses adhere to a predefined JSON structure, the 'AI_COMPLETE function's 'response_format' argument, which accepts a JSON schema, is the most effective and direct method. This mechanism enforces the structure, data types, and required fields, significantly reducing the need for post-processing and ensuring deterministic, high-quality output. The AI-Infused Data Pipelines with Snowflake Cortex blog highlights asking the LLM to create a JSON object for maximizing utility. While setting 'temperature' to 0 can improve consistency, it does not enforce a specific schema. Prompt engineering (Option A) can help but does not guarantee strict adherence. Using multiple extraction calls (Option D) is less efficient and robust for extracting multiple related fields than a single 'AI_COMPLETE call with a structured output schema. Snowflake Cortex does not automatically infer and enforce a JSON schema without explicit configuration (Option E).

NEW QUESTION # 14
A data engineer is building a robust pipeline to process customer feedback. They need to extract specific sentiment categories (food_quality, food_taste, wait_time, food _cost) from text reviews and ensure the output is always a valid JSON object matching a predefined schema, even for complex reviews. They also want to control the determinism of the LLM responses. Which of the following SQL statements or considerations are correct for achieving this using Snowflake Cortex AI functions?

A. The response_format argument with a JSON schema is primarily for OpenAl (GPT) models; for other models like Mistral, a strong prompt instruction such as 'Respond in strict JSON' is generally more effective.
B. To ensure the model explicitly attempts to extract all specified fields, the 'required' array in the JSON schema is critical; AI_COMPLETE will raise an error if any required field cannot be extracted.
C. The following SQL statement uses the response_format argument and temperature setting to achieve structured output and determinism:
D. Using AI_COMPLETE with response_format incurs additional compute cost for the overhead of verifying each token against the supplied JSON schema, in addition to standard token costs.
E. For the most consistent structured output, especially in complex reasoning tasks, setting the temperature option to 0 when calling AI_COMPLETE is recommended.

Answer: B,C,E

Explanation:
Option A is correct because it demonstrates the proper use of the 'AI_COMPLETE function with the 'response_format' argument to specify a JSON schema and sets 'temperature' to 0 for consistent output, as per the documentation. Option C is correct as the "required'' field in the JSON schema ensures that specific fields must be extracted, and 'COMPLETE (or 'AI_COMPLETE) will raise an error if these fields cannot be found. Option E is correct because for the most consistent results, setting the 'temperature* option to O is recommended when calling 'COMPLETE (or "AI_COMPLETE) with structured outputs, regardless of the task or model. Option B is incorrect because all models supported by support structured output, and specifying the 'response_format' is the direct mechanism to enforce a schema, although for complex tasks, adding 'Respond in JSON' to the prompt can improve accuracy. Option D is incorrect as 'AI_COMPLETE Structured Outputs incurs compute cost based on the number of tokens processed, but it does not incur additional compute cost for the overhead of verifying each token against the supplied JSON schema.

NEW QUESTION # 15
A data engineer is integrating SNOWFLAKE. CORTEX. CLASSIFY_TEXT into an automated data pipeline that uses dynamic tables to process and transform streaming text dat a. They have ensured that the service account used has been granted the necessary SNOWFLAKE. CORTEX_USER database role. After deploying the pipeline, they consistently receive an error whenever CLASSIFY_TEXT is invoked. Which of the following is the most likely cause of the error encountered by the data engineer?

A. The input text being processed by 'CLASSIFY _ TEXT includes extensive non-plain English content, such as code blocks, which causes the function to fail with an error.
B. The role used by the data engineer, despite having 'SNOWFLAKE.CORTEX_USER, lacks the fundamental 'USAGE privilege on the database where the text data is stored.
C. The 'task_description' provided in the optional arguments for 'CLASSIFY_TEXT exceeds the recommended length of approximately 50 words, leading to a validation error.
D. Snowflake Cortex functions, including 'CLASSIFY_TEXT , currently do not support integration with dynamic tables within data pipelines.
E. The array contains more than 100 unique categories, exceeding the maximum allowed limit for the function.

Answer: D

Explanation:
Option A is plausible for a data-specific error, but the question describes a 'consistent error' during pipeline integration. The maximum number of categories is 100. Option B is incorrect because if the text contains non-plain English content like code snippets, the function 'won't return an error, but the results may not be what you expect'. This would lead to inaccurate results, not a consistent error preventing the function's execution. Option C is less likely to be the 'most' likely cause of an error specific to the 'CLASSIFY_TEXT function's invocation, especially since the 'SNOWFLAKE.CORTEX_USER role, which grants access to Cortex AI functions, has already been granted. Missing 'USAGE on the data's database would typically manifest as a more general SQL access error. Option D is correct because a known limitation for Snowflake Cortex functions, including "CLASSIFY _ TEXT , is that they do not support dynamic tables. This is a fundamental incompatibility that would cause consistent errors when integrating into a dynamic table pipeline. Option E is incorrect. While a 'task_description' should be 'no more than about 50 words', this is a recommendation for optimal performance, not a strict limit that is explicitly stated to cause an error when exceeded.

NEW QUESTION # 16
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