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購買後，立即下載 GES-C01 題庫 (SnowPro® Specialty: Gen AI Certification Exam): 成功付款後, 我們的體統將自動通過電子郵箱將您已購買的產品發送到您的郵箱。（如果在12小時內未收到，請聯繫我們，注意：不要忘記檢查您的垃圾郵件。）

最新的 Snowflake Certification GES-C01 免費考試真題:

1. data scientist is designing a Retrieval Augmented Generation (RAG) system in Snowflake for querying a large knowledge base of internal documents. They plan to store document embeddings and use vector similarity for retrieval. Which statement accurately describes the use of vector functions and associated costs in this RAG architecture?

A) Storing vector embeddings, created using functions like

B) Snowpark Python fully supports all Snowflake vector similarity functions, including

C)

D) The

E) When using

2. 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.)

A) Option E
B) Option B
C) Option C
D) Option A
E) Option D

3. A development team is preparing to deploy a new Retrieval-Augmented Generation (RAG) application written in Python. They intend to use Snowflake AI Observability to capture detailed logs and traces for debugging and performance analysis. Which of the following configurations are essential prerequisites for enabling this logging capability effectively?

A) Option E
B) Option B
C) Option C
D) Option A
E) Option D

4. A Snowflake developer is tasked with enhancing a daily data pipeline. The pipeline processes raw text descriptions of system events and needs to extract structured information, specifically the (string) and its (string, restricted to 'low', 'medium', 'high', 'critical'). The output must be a strictly formatted JSON object, ensuring data quality for downstream analytics.
Consider the following SQL snippet intended for this transformation:

Which of the following statements are correct regarding this implementation and best practices for using with structured outputs in a data pipeline?

A) The complexity of the JSON schema, particularly deep nesting, does not impact the number of tokens processed and billed for 'AI_COMPLETE Structured Outputs.
B) Setting 'temperature 'to '0.7 ' is optimal for ensuring the most consistent and deterministic JSON outputs, especially for complex extraction tasks.
C) For all models supported by 'AI_COMPLETE' Structured Outputs, the 'additionalPropertieS field must be set to 'false' in every node of the schema, and the 'required' field must contain all property names.
D) The 'response_format' correctly defines the expected JSON structure, using 'enum' for 'severity_lever and 'required' to ensure 'event_name' and severity_lever are always present if extracted.
E) Using 'TRY COMPLETE instead of would allow the pipeline to gracefully handle cases where the model fails to generate a valid JSON response by returning 'NULL' instead of an error.

5. A Gen AI Specialist is leveraging Snowflake Document AI to extract specific entities and table data from a large and varied collection of documents. They are aware of potential limitations and want to understand the expected outcomes when processing different types of files. Considering a scenario where a Document AI model build is used with the '!PREDICT' method, which of the following statements accurately describe the expected behavior or potential issues based on Document AI's conditions and limitations?

A) Processing a legal contract document that is 130 pages long will likely result in a '_processingErrors' message indicating that the document has too many pages.
B) If the extracted answer to a question for a single entity (e.g., is very long, it will be automatically truncated to a maximum of 2048 tokens.
C) In a table extraction task, if a specific cell (e.g., 'tablellitem') is empty, the resulting JSON will omit the 'value' key for that cell, but will still provide a 'score' indicating the model's confidence that the cell is empty.
D) If a question for an entity, like 'total_invoice_amount', does not find a corresponding value in a document, the JSON output for will contain a 'value' key with a 'null' string and a 'score' key indicating the model's confidence in the absence of the answer.
E) A document written entirely in Ukrainian will be processed by Document AI, and the extracted information will be of satisfactory quality due to extensive multilingual support.

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