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>> GES-C01最新考題 <<

最新的 Snowflake Certification GES-C01 免費考試真題 (Q267-Q272):

問題 #267
A data analytics team is building a self-service analytics application using Snowflake Cortex Analyst to allow business users to query sales data with natural language. They are defining a semantic model in YAML to ensure accurate text-to-SQL generation. Which of the following is the most crucial aspect of the semantic model's configuration for Cortex Analyst to effectively translate natural language into SQL for structured data?

A. Utilizing advanced data types like 'VARIANT' and 'OBJECT for all dimensions to accommodate semi-structured data without complex transformations.
B. Providing detailed 'name', 'description' , and 'synonyms' for logical tables, dimensions, and facts to bridge the gap between business terminology and the underlying database schema.
C. Configuring the 'base_table' parameter to directly reference a dynamic table, ensuring real-time data ingestion and processing before SQL generation.
D. Defining a comprehensive 'verified_queries' section with a high volume of example natural language questions and their exact SQL translations to handle all potential user queries.
E. Specifying a dedicated 'CORTEX SEARCH SERVICE for every dimension to pre-compute all possible literal values, optimizing response time.

答案：B

解題說明：
Option C is correct because the primary purpose of a semantic model in Cortex Analyst is to provide semantic information about your data, bridging the gap between business users' natural language and the technical database schema. This includes using descriptive names, synonyms, and descriptions for logical tables, dimensions, and facts, which is essential for Cortex Analyst to reliably generate accurate SQL from natural language questions. Option A is incorrect; while Cortex Search Services can improve literal matching for dimensions, it's an enhancement and not the most crucial foundational aspect of the semantic model for general text-to-SQL translation, nor is it required for 'every' dimension. Option B is incorrect because while 'verified_queries' improve accuracy for similar questions, a high volume of examples for 'all' potential queries is not feasible or the most crucial initial configuration; the core mapping (Option C) is more fundamental. Option D is incorrect as the 'base_table' must refer to a physical table or view, not directly to a dynamic table. Furthermore, Cortex functions do not support dynamic tables directly. Option E is incorrect because 'VARIANT, 'OBJECT, 'GEOGRAPHY , and 'ARRAY data types are explicitly not supported for dimension, fact, or metric columns in a semantic model.

問題 #268
A data science team is planning to implement a new RAG (Retrieval Augmented Generation) application using Snowflake Cortex, specifically leveraging Cortex Search. They are evaluating the key features, best practices, and cost considerations associated with Cortex Search. Which of the following statements accurately describe aspects of Cortex Search?

A. Cortex Search Services require a virtual warehouse for initial setup and subsequent refreshes to run queries against base objects and build the search index.
B. For best search results, Snowflake recommends splitting text in the search column into chunks of no more than 512 tokens, even when longer-context embedding models are available.
C. Cortex Search automatically handles embedding, infrastructure maintenance, and ongoing index refreshes, and can be used as a backend for enterprise search or a RAG engine for LLM chatbots.
D. The credit cost for Cortex Search Services is primarily based on the number of queries executed against the service, not the amount of indexed data.
E. Cortex Search supports only English-only embedding models; multilingual RAG applications require external embedding solutions.

答案：A,B,C

解題說明：
Option A is correct. Cortex Search provides low-latency, high-quality 'fuzzy' search and handles embedding, infrastructure maintenance, search quality parameter tuning, and ongoing index refreshes. Its primary use cases are as a RAG engine for LLM chatbots and as a backend for enterprise search. Option B is incorrect. Cortex Search Services incur costs based on the amount of indexed data (6.3 Credits per GB/mo of indexed data), not solely on the number of queries executed. Option C is incorrect. Cortex Search offers multilingual embedding models like 'snowflake-arctic-embed-l-v2.ff and 'voyage-multilingual-2 , supporting multilingual AI workflows. Option D is correct. Snowflake recommends splitting text into chunks of no more than 512 tokens for optimal search results, as smaller chunks can lead to more precise retrieval and higher-quality LLM responses in RAG scenarios, even with models that support longer context windows. Option E is correct. A virtual warehouse is required for Cortex Search Service to refresh the service, which includes running queries against base objects, orchestrating text embedding jobs, and building the search index.

問題 #269
A global marketing team uses Snowflake to manage customer feedback in various languages. They need to translate customer reviews from German ("de") into English ("en") for analysis. The reviews are stored in a table named 'CUSTOMER REVIEWS' in a column called 'REVIEW TEXT'. Which of the following SQL statements correctly applies the 'SNOWFLAKE.CORTEX.TRANSLATE function and what is the expected return type for the translated text?

A. The query
B. The query
C. The query
D. The query
E. The query

答案：B

解題說明：
Option B is correct. The 'SNOWFLAKE.CORTEX.TRANSLATE function takes three arguments: the text to be translated, the source language, and the target language. It returns a STRING value containing the translated text. Option A includes an unsupported 'high_accuracy' option and claims an incorrect return type. Option C uses an incorrect syntax and claims an incorrect return type. Option D uses an incorrect number of arguments. Option E claims an incorrect return type.

問題 #270
A data application developer is building a Streamlit chat application within Snowflake. This application uses a RAG pattern to answer user questions about a knowledge base, leveraging a Cortex Search Service for retrieval and an LLM for generating responses. The developer wants to ensure responses are relevant, concise, and structured. Which of the following practices are crucial when integrating Cortex Search with Snowflake Cortex LLM functions like AI_COMPLETE for this RAG chatbot?

A. To maintain conversational context in a multi-turn chat, the developer should pass all previous user prompts and model responses in the
B. The retrieved context from Cortex Search should be directly concatenated with the user's prompt as input to the
C. The
D. For performance and cost optimization, it is always recommended to query Cortex Search and the LLM function within a single
E. Using the

答案：A,E

解題說明：

問題 #271
An ML Engineer has developed a custom PyTorch model for image processing that requires GPU acceleration and specific PyPl packages ('torch' , 'torchvision'). They want to deploy it as a service on Snowpark Container Services (SPCS) using the Snowflake Model Registry. Which of the following statements are true regarding the deployment of this model to SPCS and its requirements? (Select all that apply.)

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

答案：A,B,D

解題說明：
StatementA is incorrect. While Snowflake recommends using only 'conda_dependencies' or only 'pip_requirements' (not both) to avoid package conflicts, the scenario explicitly mentions PyPl packages. If using 'pip_requirements', all required packages should be listed there. The example incorrectly assumes 'torchvision' would necessarily be best sourced from Conda and dictates avoiding 'pip_requirements' entirely, which is an oversimplification of the recommendation. Statement B is correct. To utilize GPU acceleration in SPCS, a compute pool configured with a GPU instance family (e.g., *GPU must be created and then referenced by name in the 'service_compute_poor' argument when creating the service. Statement C is correct. Snowflake's warehouse nodes have restricted directory access, and '/tmpP is recommended as a safe and writeable location for models that need to write files during execution. This principle extends to SPCS containers. Statement D is correct. The 'create_service' method for deploying models to SPCS takes a gpu_requests argument, which specifies the number of GPUs to allocate to the service. Setting this (e.g., to "s) is crucial for ensuring the model runs on GPU hardware. Statement E is incorrect. The 'relax_version' option, which modifies version constraints, defaults to 'True' in 'log_moder' While often beneficial, it is not mandatory to explicitly set it to 'True' for every deployment scenario.

問題 #272
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Fast2test能夠幫你簡單地通過Snowflake GES-C01認證考試，如果官方改變了認證考試的大綱，我們會立即通知客戶，因此，只要你好好學習這個考古題，通過GES-C01考試就會非常容易，這里有大量的學習資料試題和答案，是滿足嚴格質量標準的考試題庫，涵蓋所有的Snowflake GES-C01考試知識點。

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值得信賴的GES-C01最新考題＆資格考試和認證領導者&Snowflake SnowPro® Specialty: Gen AI Certification Exam

最新GES-C01考題 & GES-C01考試

最新的 Snowflake Certification GES-C01 免費考試真題 (Q267-Q272):

高質量的GES-C01最新考題和資格考試中的領導者和完整覆盖的Snowflake SnowPro® Specialty: Gen AI Certification Exam

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