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

1. A multi-national corporation uses Snowflake across several AWS regions. Their primary operational Snowflake account is in AWS US East (Ohio), but they need to leverage a specific AI_COMPLETE model, llama4-maverick, which is natively available in AWS US East 1 (N. Virginia) but not in US East (Ohio). To address this, the Snowflake administrator enables cross-region inference for their US East (Ohio) account.

A) Cross-region inference is fully supported for AI_COMPLETE in U.S. SnowGov regions for both inbound and outbound inference requests, provided the target model is available in the respective SnowGov region.
B) To enable cross-region inference for the US East (Ohio) account, the administrator would execute the command: ALTER ACCOUNT SET AWS_US' ; to allow inference requests to be processed in any AWS US region where the model is available. CORTEX_ENABLED_CROSS REGION =AWS_US' ;
C) The query latency for cross-region inference with AI_COMPLETE is consistently low and predictable, as Snowflake's architecture is designed to completely negate the impact of geographical distance and network variations.
D) The llama4-maverick model is listed as natively available in AWS US East 1 (N. Virginia) and is supported for cross-region inference (AWS US Cross-Region), validating it as a suitable target for inference from US East (Ohio).
E) User inputs, service-generated prompts, and the generated outputs from cross-region AI_COMPLETE calls are automatically stored or cached in the remote processing region to optimize performance for subsequent identical requests.

2. A data scientist is implementing a Retrieval Augmented Generation (RAG) system in Snowflake for a legal document repository. They need to convert legal document chunks into vector embeddings and efficiently find the most relevant document chunks based on a user's query. Which of the following statements accurately describe the process and best practices for creating and using these vector embeddings with Snowflake Cortex LLM functions?

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

3. A data processing team is using Snowflake Document AI to extract data from incoming supplier invoices. They observe that many documents are failing to process, and successful extractions are taking longer than expected, leading to increased costs. Upon investigation, they find error messages such as

. Additionally, their 'X-LARGE virtual warehouse is constantly active, contributing to higher-than-anticipated bills. Which two of the following actions are essential steps to troubleshoot and address the root causes of these processing errors and optimize their Document AI pipeline?

A) Scale down the virtual warehouse to 'X-SMALL' or 'SMALL' size, as larger warehouses do not increase Document AI query processing speed and incur unnecessary costs.
B) Increase the 'max_tokens' parameter within the '!PREDICT' function options to accommodate longer document responses from the model.
C) Configure the internal stage used for storing invoices with 'ENCRYPTION = (TYPE = 'SNOWFLAKE SSE')'.
D) Implement a pre-processing step to split documents exceeding 125 pages or 50 MB into smaller, compliant files before loading to the stage.
E) Redefine extraction questions to be more generic and encompassing, reducing the number of distinct questions needed per document.

4. A data engineer is setting up a Document AI pipeline to extract information from scanned invoices stored in an internal stage named 'invoice_stage'. They have created the stage using 'CREATE STAGE and uploaded several PDF documents. However, when attempting to run the extraction query, they encounter an error message: 'File extension does not match actual mime type. Mime- Type: application/octet-stream'. Additionally, they anticipate a privilege issue might arise for pipeline automation. Which of the following conditions must be met to resolve the current error and ensure proper setup for Document AI extraction and subsequent pipeline creation?

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

5. A data engineer is tasked with implementing a product recommendation system in Snowflake. They have pre-computed product embeddings and want to find similar items using VECTOR_COSINE_SIMILARITY They are evaluating options for interacting with this function. Which of the following statements is TRUE regarding the use of VECTOR_COSINE_SIMILARITY and Snowflake's VECTOR data type?

A)

B) A column defined as

C) Direct comparison operators like

D) The maximum dimension supported by the

E) The

問題與答案：