IT業界で広く認められるSnowflake SnowPro® Specialty: Gen AI Certificationは、Snowflake公式の認定資格です。JPNTestのGES-C01練習問題はSnowflakeの公式出題ガイドに基づいて構成されており、351の問題で試験範囲を体系的に学べます。

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Snowflake SnowPro® Specialty: Gen AI Certification 認定 GES-C01 試験問題：

問題 #1

A business intelligence team wants to enable non-technical users to query their Snowflake data using natural language for sales analytics reports via Cortex Analyst. They are designing the YAML semantic model. Which of the following statements accurately describe key aspects of designing and utilizing a semantic model for Cortex Analyst?

A. facts in a semantic model are primarily used to define categorical data, such as product types or customer segments, to support filtering operations.
B. To optimize performance, Snowflake recommends including all available tables and columns from the underlying database in a semantic model, especially for complex analytical tasks.
C. Dimensions in the semantic model YAML, such as 'state' or 'product_category', can include synonyms to map common business terms to underlying technical column names, thereby improving natural language understanding for users.
D. The base_table field in a logical table definition must directly reference a physical table and cannot point to a view, as Cortex Analyst only works with raw tables for performance reasons.
E. The VARIANT, OBJECT, GEOGRAPHY, and ARRAY data types are fully supported for dimension and fact columns within a semantic model, offering flexibility for diverse data structures.

問題 #2

A Snowflake administrator is tasked with monitoring the efficiency and cost-effectiveness of their Cortex Analyst deployments. They need to identify if certain semantic models are generating a high volume of failed or expensive queries. Which of the following approaches or statements are crucial for effectively monitoring and identifying issues with Cortex Analyst usage and associated costs?

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

問題 #3

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. 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.
B. 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.
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. 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.

問題 #4

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

問題 #5

A marketing team is analyzing social media comments using Snowflake and wants to categorize them into predefined campaign sentiments (e.g., 'Positive Campaign Engagement', 'Negative Campaign Feedback', 'Neutral Discussion'). They decide to use the SNOWFLAKE. CORTEX. CLASSIFY TEXT function for this task. Which of the following statements about its usage are correct?

A. To provide more context and potentially improve classification accuracy, categories within the can be defined as SQL objects, including 'description' and 'examples' fields.
B. If the input text exceeds a model-specific token limit, CLASSIFY_TEXT will automatically truncate the text before processing without raising an error.
C. The input string to CLASSIFY_TEXT is case-insensitive, meaning 'Great product!' and 'great product!' will yield identical classification results due to automatic normalization.
D. CLASSIFY_TEXT can return a JSON object with a 'label' field, where the value of this field indicates the classified category of the input text.
E. The argument must contain exactly two string values for effective binary classification, otherwise an error is returned.

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