Snowflake GES-C01 問題集 練習問題・模擬試験｜日本語版＆英語版対応

サンプル問題 1： A data architect is integrating Snowflake Cortex LLM functions into various data enrichment pipelines. To ensure optimal performance, cost-efficiency, and accuracy, which of the following are valid best practices or considerations for these pipelines?

• A: When extracting specific entities from documents using SAI EXTRACT or '!PREDICT, it is often more effective to fine-tune a Document AI model for complex or varied document layouts rather than relying solely on extensive prompt engineering for zero-shot extraction.
• B: For tasks requiring deterministic JSON outputs, explicitly specifying a JSON schema using the ‘response_format' argument with 'AI COMPLETE is crucial, and for OpenAI (GPT) models, including the ‘required' field and setting ‘additionalPropertieS to 'false' in every node of the schema is a mandatory requirement.
• C: To manage costs effectively for LLM functions like SAI COMPLETE in a pipeline, always use the largest available warehouse size (e.g., 6XL Snowpark- optimized) to maximize throughput, as this directly reduces the overall token processing time and cost.
• D: When performing sentiment analysis on customer feedback using 'AI_SENTIMENT, it's best practice to pass detailed, multi-turn conversation history to the function to enhance accuracy, similar to how 'AI_COMPLETE handles conversational context.
• E: For data enrichment involving classification with 'AI_CLASSIFY', using descriptive and mutually exclusive categories in plain English, along with an optional clear task description, can significantly improve classification accuracy.

サンプル問題 2： An ML engineering team is preparing to log a custom Python model to the Snowflake Model Registry. This model has several Python package dependencies. The team wants to ensure the model can be deployed optimally, either in a Snowflake warehouse or to Snowpark Container Services (SPCS), depending on future needs. They are particularly concerned with how dependency specification impacts deployment eligibility. Which statements accurately describe how Snowflake handles model dependencies and determines deployment eligibility for custom Python models logged in the Model Registry, particularly when considering both Snowflake warehouse and Snowpark Container Services (SPCS) environments? (Select all that apply.)

• A: If all of a model's ‘conda_dependencieS are available in the Snowflake conda channel, the model is automatically deemed eligible to run in a warehouse.
• B: For models intended for SPCS, ‘pip_requirements’ are always preferred over ‘conda_dependencies’ because SPCS strictly prohibits the use of any conda packages from ‘conda-forge’
• C: When ‘conda_dependencies’ are specified for a model to be deployed to SPCS, these dependencies are by default obtained from ‘conda-forge’ rather than the Snowflake conda channel.
• D: The function will fail if 'WAREHOUSE is specified in ‘target_platforms’ but the model's size or GPU requirements make it ineligible for warehouse deployment.
• E: Specifying both ‘conda_dependencies’ and ‘pip_requirements’ for a model is recommended to cover all possible deployment scenarios, and Snowflake's build process ensures compatibility between them.

サンプル問題 3： A team is designing a complex Gen AI application in Snowflake, which includes components for training a custom LLM, running batch inference, and providing a real-time conversational interface. They plan to leverage Snowpark Container Services (SPCS) for these workloads. Which of the following statements accurately describe the suitable SPCS service design models and important considerations for these different application components? (Select all that apply.)

• A: GPU-accelerated LLM training, which is a finite and often resource-intensive task, is best implemented as a ‘‘job’’ in SPCS, invoked via "EXECUTE JOB SERVICE', as it is designed to run to completion and then spin down.
• B: Real-time LLM inference for a conversational interface is ideally deployed as a ‘ ‘Service’ ‘ in SPCS, which is long-running and accessible via an HTTP endpoint, ensuring continuous availability and responsiveness.
• C: For batch inference on Snowflake data where data locality and efficiency are key, using ‘‘Service Functions’’ is highly efficient because data is passed as input parameters directly from SQL queries, and this design ensures the data never leaves the Snowflake network boundary.
• D: When deploying LLMs to SPCS, it's generally most cost-efficient to use generic CPU instance types like 'CPU X64 XS' for all tasks, as GPU instances (e.g., are exclusively for highly specialized computer vision tasks and not optimized for LLMs.
• E: Container images for SPCS deployments are typically pushed to a public Docker Hub repository, and Snowflake pulls them as needed during service creation and scaling, simplifying image management.