[2025年12月04日]GES-C01試験問題集でSnowflake練習テスト問題 [Q136-Q151]

[2025年12月04日]GES-C01試験問題集でSnowflake練習テスト問題

最新でリアルなGES-C01試験問題集解答

質問 # 136
A legal department uses Snowflake to manage and review large volumes of contracts. They need to automate the process of finding specific pieces of information, such as the effective_date or involved_parties, from these unstructured contract texts. They are considering using SNOWFLAKE. CORTEX. EXTRACT_ANSWER. Which characteristic correctly describes the primary intent or behavior of SNOWFLAKE. CORTEX. EXTRACT_ANSWER, distinguishing it from other LLM functions?

• A. It classifies an input text into one of several predefined categories provided by the user.
• B. It focuses on identifying and returning a direct answer to a specific question contained within the provided source document.
• C. It transforms an input document into a summarized version, reducing its length while preserving key information.
• D. It evaluates a text and returns a numeric score indicating the overall positive or negative sentiment.
• E. It is primarily designed to generate entirely new text based on a given prompt, much like a conversational AI.

正解：B

解説：
Option D is correct. The 'SNOWFLAKE.CORTEX.EXTRACT_ANSWER function is specifically designed to extract an answer to a given question from a text document. Option A describes the 'COMPLETE function. Option B describes the 'SENTIMENT function. Option C describes the 'CLASSIFY_TEXT function. Option E describes the 'SUMMARIZE function.

質問 # 137
A financial institution is fine-tuning a llama3.1-70b model within Snowflake Cortex using sensitive internal financial reports to improve sentiment analysis on earnings call transcripts. They need to understand the implications for data privacy, model ownership, and how this fine-tuned model can be managed and shared. Which of the following statements are true regarding this process?

• A. The financial reports used for fine-tuning the llama3.1-7b model are securely isolated and are not used by Snowflake to train or re-train models for other customers.
• B. The fine-tuning process requires the explicit provisioning and management of a Snowpark-optimized warehouse with GPU resources by the institution.
• C. Fine-tuned LLMs built with Cortex Fine-tuning are fully managed through the Snowflake Model Registry API, allowing for programmatic deployment, version control, and comprehensive lifecycle management.
• D. The fine-tuned model, being of type CORTEX_FINETUNED, can be shared with other Snowflake accounts using secure data sharing capabilities.
• E. The resulting fine-tuned model (e.g., my_sentiment_model) is the exclusive property of the financial institution and cannot be accessed or used by any other Snowflake customer.

正解：A、D、E

解説：
Option A is correct. Snowflake's privacy principles state that your Usage and Customer Data (including inputs and outputs for fine- tuning) are NOT used to train, re-train, or fine-tune Models made available to others. Option B is correct. Fine-tuned models built using your data can only be used by you, ensuring exclusivity. Option C is correct. Models generated with Cortex Fine-tuning (specifically of the type) can be shared using Data Sharing. Option D is incorrect. While Cortex Fine-Tuned LLMs appear in the Model Registry's Snowsight UI, they are *not* managed by the Model Registry API. Option E is incorrect. Cortex Fine-tuning is described as a 'fully managed service' within Snowflake, which abstracts away much of the underlying infrastructure management like GPU resources, although a warehouse is selected for the job. Explicit provisioning and management of compute pools with GPUs is more characteristic of Snowpark Container Services for custom models.

質問 # 138
A data engineering team is setting up an automated pipeline to extract information from new invoices using Document AI. They've created a database and schema Cinvoice_db.invoice_schema') and a Document AI model build ('invoice_extractofl). They then created an internal stage for documents. When they attempt to run the method on documents uploaded to 'invoice_stage' , they consistently receive the following error:

Given this error message, which 'corrective SQL command' addresses the most likely misconfiguration of the 'invoice_stage' to allow Document AI processing?

• A.
• B.
• C.
• D.
• E.

正解：B

解説：

質問 # 139
A company is planning to process a large volume of legal documents to generate summaries using SNOWFLAKE. CORTEX. SUMMARIZE. Given the scale, they are acutely focused on managing costs and optimizing performance. Which of the following statements are true regarding the cost and performance characteristics of using SNOWFLAKE. CORTEX. SUMMARIZE? (Select all that apply)

• A. The fixed billing rate for the SUMMARIZE function is 0.10 Credits per one million Tokens processed.
• B. Snowflake recommends using a larger warehouse (e.g., L or XL) for SUMMARIZE function calls to significantly improve processing performance for high-volume tasks.
• C. For SUWARIZE, Snowflake adds an internal prompt to the user's input text, which increases the total input token count for billing purposes beyond the raw text length.
• D. The context window for the SWIMARIZE function is 4,096 tokens, ensuring efficiency for short documents only.
• E. The SUWARIZE function is billed primarily based on the number of output tokens generated in the response, not input tokens.

正解：A、C

解説：
Options B and D are correct. - is correct": For 'SUMMARIZE, 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. - is correct'': The cost for the Summarize' function is 0.10 Credits per one million Tokens processed. -A is incorrect: For functions that generate new text in the response, such as 'SUMMARIZE' , both input and output tokens are billable. - C is incorrect: Snowflake recommends executing queries that call a Snowflake Cortex AISQL function, including SUMMARIZE , with a smaller warehouse (no larger than MEDIUM), as larger warehouses do not increase performance for these operations. - E is incorrect: The context window for the Snowflake managed model from the 'SUMMARIZE function is 32,000 tokens, not 4,096 tokens.

質問 # 140
A data engineer is integrating a custom application with Snowflake Cortex to leverage the 'COMPLETE' function via its REST API. They are preparing a 'curl' request to send a prompt to the 'mistral-large? model. Which of the following 'curl' command configurations correctly specifies the ''mandatory'' authentication header and a valid token type for accessing the Cortex REST API?

• A.
• B.
• C.
• D.
• E.

正解：D

解説：
The Cortex REST API requires the 'Authorization: Bearer ' header for authentication. The token can be a JSON web token (JWT), OAuth token, or programmatic access token. Option B correctly uses 'Authorization: Bearer ' and includes the other required headers ('Content- Type' and 'Accept') for a 'COMPLETE request. Options A, C, D, and E use incorrect header names or token types for the 'Authorization' header according to the documentation.

質問 # 141
An ML engineer is deploying a custom PyTorch-based image classification model, obtained from Hugging Face, to Snowpark Container Services (SPCS). The deployment requires GPU acceleration on a compute pool named 'my_gpu_pool' and specific Python packages ('torch' , 'transformers' , 'opencv-python'). The scenario dictates that 'opencv-python' is only available via PyPl, while 'torch' and 'transformers' can be sourced from either conda-forge or PyPl. The engineer uses the Snowflake Model Registry to log the model. Which of the following and configurations correctly specify the necessary Python dependencies and GPU utilization for this inference service, adhering to Snowflake's recommendations?

• A.
• B.
• C.
• D.
• E.

正解：C

解説：
Option A is correct. The 'pip_requirementS argument can be used to specify all necessary Python packages, including 'torch 'transformers' , and 'opencv-python' , which are commonly available on PyPl. The 'create_service' call correctly specifies and to leverage GPU acceleration. This approach aligns with the Snowflake recommendation to use either 'conda_dependencieS or 'pip_requirementS , but not both, for dependency management. Option B is incorrect because 'opencv-python' is specified as only available via PyPl in the scenario, meaning it cannot be installed via 'conda-forge' . Option C is incorrect because is chosen, which will not provide GPU acceleration required by the model. Option D is incorrect because 'opencv-python' is not available through Anaconda channels (as per the scenario that it is PyPl only), and for other conda packages, explicitly specifying the 'conda-forge' channel (e.g., is the recommended practice for SPCS dependencies if they are not in the Snowflake Anaconda channel. Option E is incorrect because, while it correctly separates conda and pip dependencies, Snowflake explicitly recommends 'using only 'conda_dependencieS or only 'pip_requirementss , not both' for managing dependencies to avoid potential conflicts.

質問 # 142
A data engineering team is optimizing an AI-infused pipeline that processes millions of rows of customer interaction data in a LOG_DATA table using various Snowflake Cortex AI functions. They need to accurately estimate costs and ensure optimal performance. Which of the following statements regarding cost, performance, and operational considerations for these functions are true?

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

正解：A、C、D

解説：

質問 # 143
A data engineering team is building an automated pipeline in Snowflake to process customer reviews. They need to use AI_COMPLETE to extract specific details like product, sentiment, and issue type, and store them in a strictly defined JSON format for seamless downstream integration. They aim to maximize the accuracy of the structured output and manage potential model limitations. Which statements accurately reflect the best practices and characteristics when using AI_COMPLETE with structured outputs for this scenario?

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

正解：A、B、D

解説：
Option A is incorrect because Structured Outputs do not incur additional compute cost for the overhead of verifying each AI_COMPLETE token against the supplied JSON schema, though the number of tokens processed (and thus billed) can increase with schema complexity. Option B is correct because for complex reasoning tasks, it is recommended to use the most powerful models and explicitly add 'Respond in JSON' to the prompt to optimize accuracy. Option C is correct as for OpenAI (GPT) models, the schema has specific requirements: response_format must be set to in every node, and the required field must include the names of every property in the schema. Option D additional Properties false is incorrect because AI_COMPLETE verifies each generated token against the JSON schema to ensure conformity, and if the model cannot generate a response that matches the schema, it will result in a validation error. Option E is correct as setting the option to e is recommended for temperature the most consistent results, regardless of the task or model, especially for structured outputs.

質問 # 144
A data science team is fine-tuning a Snowflake Document AI model to improve the extraction accuracy of specific fields from a new type of complex legal document. They are consistently observing low confidence scores and inconsistent 'value' keys for extracted entities, even after initial training. Which two of the following best practices should the team follow to most effectively improve the model's extraction accuracy and confidence for this complex document type?

• A. Prioritize extensive prompt engineering by creating highly detailed and complex questions with intricate logic to guide the LLM's understanding of the extraction task.
• B. Limit the fine-tuning training data exclusively to perfectly formatted and clean documents to ensure the model learns from ideal examples without noise.
• C. Actively involve subject matter experts (SMEs) or document owners throughout the iterative process to help define data values, provide annotations, and evaluate the model's effectiveness.
• D. Set the 'temperature' parameter to a higher value (e.g., 0.7) during '!PREDICT calls to encourage more creative and diverse interpretations by the model.
• E. Ensure the training dataset used for fine-tuning includes diverse documents representing various layouts, data variations, and explicit examples of 'NULL' values or empty cells where appropriate.

正解：C、E

解説：
To improve Document AI model training, it is crucial to ensure that the documents uploaded for training represent a real use case and that the dataset consists of diverse documents in terms of both layout and data. If all documents contain the same data or are always presented in the same form, the model might provide incorrect results. For table extraction, it is vital that enough data is used to train the model to include values and maintain order. Therefore, ensuring a diverse training dataset (Option B) is a key best practice. Additionally, Subject Matter Experts (SMEs) and document owners are crucial partners in understanding and evaluating the model's effectiveness in extracting the required information. Their involvement in defining data values, providing annotations, and evaluating results will significantly improve accuracy (Option C). Option A is not a best practice; it's recommended to keep questions as encompassing as possible and rely on training with annotations rather than complex prompt engineering, especially for document variability. Option D is incorrect; a higher 'temperature' value increases the randomness and diversity of the model's output, which is generally undesirable for accurate data extraction where deterministic results are preferred. For most consistent results, 'temperature' should be set to 0. Option E is incorrect because training on a restricted set of perfectly formatted documents can lead to a model that performs poorly on real-world, varied documents; diversity in training data is essential.

質問 # 145
A Data Engineer has successfully deployed a Document AI model build named 'expense reports' to extract 'total amount' and approver signature' from digital expense reports. They observe that sometimes the 'approver signature' is not present in a document, or certain table cells are intentionally left blank in other document types processed by Document AI. They also want to automate the ingestion and processing of new expense reports. Regarding the '!PREDICT' method's JSON output when 'approver signature' is missing or a table cell is empty, and the recommended Snowflake features for continuously processing new documents, which statements are true?

• A. To automate continuous processing, the Data Engineer should create a stream on the internal stage where documents are uploaded, and a task that calls the ' !PREDICT method when new data arrives.
• B. When a table cell is empty, the Document AI model will return a 'score' key but no 'value' key for that cell.
• C. If the model does not find an answer, it returns an empty string for 'value' and a score indicating its confidence that no answer was found.
• D. Dynamic Tables are the primary recommended feature for continuous document processing with Document AI, replacing the need for streams and tasks.
• E. If the 'approver_signature' is not found, the JSON output for 'approver_signature' will contain Tvalue" : null, "score" : 0.0}'.

正解：A、B

解説：
Option B is correct because if a table cell is empty or if the Document AI model does not find an answer in the document, it will not return a 'value' key. However, it will still return the 'score' key, indicating its confidence that the cell is empty or that the answer is not present. Option C is correct as creating document processing pipelines with Document AI typically involves setting up a stream on a stage to capture new documents and a task that then calls the '!PREDICT method to process these new documents automatically. Option A is incorrect; the 'value' key is omitted, not set to 'null'. Option D is incorrect because the 'value' key is omitted entirely, not set to an empty string. Option E is incorrect because Snowflake Cortex functions, including Document AI's '!PREDICT method, do not currently support dynamic tables.

質問 # 146
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. Configure the internal stage used for storing invoices with 'ENCRYPTION = (TYPE = 'SNOWFLAKE_SSE'Y.
• B. Implement a pre-processing step to split documents exceeding 125 pages or 50 MB into smaller, compliant files before loading to the stage.
• C. Scale down the virtual warehouse to 'X-SMALC or 'SMALL' size, as larger warehouses do not increase Document AI query processing speed and incur unnecessary costs.
• D. Redefine extraction questions to be more generic and encompassing, reducing the number of distinct questions needed per document.
• E. Increase the 'max_tokenS parameter within the ' !PREDICT' function options to accommodate longer document responses from the model.

正解：A、B

解説：
The error messages 'Document has too many pages. Actual: 130. Maximum: 125.' and File exceeds maximum size. Actual: 54096026 bytes. Maximum: 50000000 bytes.' directly indicate that the documents do not meet Document AI's input requirements, which specify a maximum of 125 pages and 50 MB file size. Therefore, implementing a pre-processing step to split or resize these documents is an essential solution (Option B). The error 'cannot identify image file <_io.Bytesl0 object at Ox...>' is a known issue that occurs when an internal stage used for Document AI is not configured with 'SNOWFLAKE_SSE encryption. Correctly configuring the stage with this encryption type is crucial for resolving this processing error (Option D). Option A, while addressing cost optimization, is not a root cause of the 'processing errors' themselves, although it is a best practice for cost governance as larger warehouses do not increase Document AI query processing speed. Option C is incorrect; best practices for question optimization suggest being specific, not generic. Option E is incorrect as 'max_tokenS relates to the length of the model's output, not the input document's size or page limits.

質問 # 147
A financial analyst wants to build a generative AI application in Snowflake that can answer complex queries by integrating financial reports (unstructured data in stages) and transaction records (structured data in tables). They decide to use Snowflake Cortex Agents. Which of the following statements accurately describe the capabilities and operational aspects of Cortex Agents in this scenario?
(Select all that apply)

• A. Cortex Agents are designed to orchestrate tasks by planning steps, utilising tools like Cortex Analyst for structured data and Cortex Search for unstructured data, and generating comprehensive responses.
• B. The primary compute cost for Cortex Agents is based on the number of tokens processed during the planning and reflection phases, with an additional per- message charge for each tool invocation.
• C. For monitoring agent interactions and performance on the client application, the TruLens Python packages (
  
• D. To provide the Agent with custom logic for specific data transformations not covered by standard tools, stored procedures or user-defined functions (UDFs) can be implemented as custom tools.
• E. When a user asks an ambiguous question, Cortex Agents utilise an 'Explore options' component to consider different permutations and disambiguate the query for improved accuracy.

正解：A、C、D、E

解説：
Option A is correct. Cortex Agents orchestrate across both structured and unstructured data sources, planning tasks, using tools (including Cortex Analyst and Cortex Search), and generating responses. Option B is correct. Stored procedures and user-defined functions (UDFs) can be used to implement custom tools for Cortex Agents. Option C is correct. The 'Explore options' component of Cortex Agents considers different permutations to disambiguate ambiguous questions, which is part of their orchestration capabilities to improve accuracy. Option D is incorrect. While Cortex Agents utilise LLMs and other Cortex features that incur token or message-based costs, the sources do not explicitly state that the *agent itself* has a primary compute cost based on 'planning and reflection phases' with an 'additional per-message charge for each tool invocation'. The cost of underlying services like Cortex Analyst is '67 Credits per 1,000 messages' and LLM calls via COMPLETE incur token costs, but this option inaccurately describes the agent's direct cost model. Option E is correct. TruLens Python packages (

) are used for monitoring Agent interaction on the client application.

質問 # 148
A Streamlit application developer wants to use AI_COMPLETE (the latest version of COMPLETE (SNOWFLAKE.CORTEX)) to process customer feedback. The goal is to extract structured information, such as the customer's sentiment, product mentioned, and any specific issues, into a predictable JSON format for immediate database ingestion. Which configuration of the AI_COMPLETE function call is essential for achieving this structured output requirement?

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

正解：A

解説：
'AI_COMPLETE Structured OutputS (and its predecessor 'COMPLETE Structured OutputS) specifically allows supplying a JSON schema as the 'response_format' argument to ensure completion responses follow a predefined structure. This significantly reduces the need for post-processing in AI data pipelines and enables seamless integration with systems requiring deterministic responses. The JSON schema object defines the structure, data types, and constraints, including required fields. For complex tasks, prompting the model to respond in JSON can improve accuracy, but the 'response_format' argument is the direct mechanism for enforcing the schema. Setting 'temperature to 0 provides more consistent results for structured output tasks. Option A is a form of prompt engineering, which can help but does not guarantee strict adherence as 'response_format does. Option B controls randomness and length, not output structure. Option D, while 'AI_EXTRACT (or EXTRACT ANSWER) can extract information, using it multiple times and then manually combining results is less efficient and less robust than a single 'AI_COMPLETE call with a structured output schema for multiple related fields. Option E's 'guardrails' are for filtering unsafe or harmful content, not for enforcing output format.

質問 # 149
A developer is building a client application that interacts with a Snowflake Cortex Agent using its REST API. They are implementing multi- turn conversation support. Which of the following is the most critical aspect for maintaining conversational context over multiple API calls?

• A. The REST API automatically identifies and reuses context from previous requests based on the Authorization token, eliminating the need to pass full history.
• B. The client application must include all prior user prompts and assistant/analyst responses in a messages array in each new API request.
• C. A specific session_id parameter, generated at the start of the conversation, must be sent with each request to link turns.
• D. Only the last user prompt and the immediately preceding agent response should be sent to conserve token usage.
• E. The agent internally summarizes long conversations, and the client receives only a condensed summary_token to pass for subsequent turns.

正解：B

解説：
Option A is the most critical and correct method for maintaining conversational state in multi-turn interactions with Cortex LLM functions, including those used by Cortex Analyst and by extension Cortex Agents. The underlying COMPLETE function (and its REST API equivalent) does not retain state from one call to the next; therefore, the client application must explicitly pass all previous user prompts and model (assistant/analyst) responses in chronological order within the 'messages' (or array for each new request to provide a stateful experience. Option B is incorrect as Cortex LLM functions do not automatically retain state across calls. Option C is incorrect; there is no documented 'session_id' parameter for implicitly managing conversation history for these APIs. Option D is incorrect; passing only partial history would lead to a loss of full conversational context and degrade the quality of follow-up responses. Option E is incorrect; while Cortex Analyst internally uses a summarization agent for long conversations to reframe questions, the client application is still responsible for managing and sending the full conversation history in the 'messages' array via the API.

質問 # 150
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. Option C
• B. Option B
• C. Option D
• D. Option E
• E. Option A

正解：C、E

解説：
Option A is correct because the account parameter is used to enable cross-region inference. Setting it CORTEX_ENABLED_CROSS_REGION to 'Aws us' would permit inference requests to be processed in any AWS US region, such as N. Virginia, from a local AWS US region like Ohio. Option B is incorrect because user inputs, service generated prompts, and outputs are explicitly not stored or cached during cross-region inference. Option C is incorrect as cross-region inference is not supported in U.S. SnowGov regions for either inbound or outbound inference requests. Option D is correct because the sources indicate that is natively available in AWS US East 1 (N. Virginia) and is supported AI_COMPLETE (llama4-maverick) for cross-region inference within AWS US regions, making it a valid target for the account in US East (Ohio). Option E is incorrect because latency between regions depends on the cloud provider infrastructure and network status, and Snowflake recommends testing specific use cases with cross-region inference enabled.

質問 # 151
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