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為你提供購買 Snowflake GES-C01 題庫產品一年免费更新，你可以获得你購買 GES-C01 題庫产品的更新，无需支付任何费用。如果我們的 Snowflake GES-C01 考古題有任何更新版本，都會立即推送給客戶，方便考生擁有最新、最有效的 GES-C01 題庫產品。

通過 Snowflake GES-C01 認證考試是不簡單的，選擇合適的考古題資料是你成功的第一步。因為好的題庫產品是你成功的保障，所以 Snowflake GES-C01 考古題就是好的保障。Snowflake GES-C01 考古題覆蓋了最新的考試指南，根據真實的 GES-C01 考試真題編訂，確保每位考生順利通過 Snowflake GES-C01 考試。

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在談到 GES-C01 最新考古題，很難忽視的是可靠性。我們是一個為考生提供準確的考試材料的專業網站，擁有多年的培訓經驗，Snowflake GES-C01 題庫資料是個值得信賴的產品，我們的IT精英團隊不斷為廣大考生提供最新版的 Snowflake GES-C01 認證考試培訓資料，我們的工作人員作出了巨大努力，以確保考生在 GES-C01 考試中總是取得好成績，可以肯定的是，Snowflake GES-C01 學習指南是為你提供最實際的認證考試資料，值得信賴。

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選擇使用 Snowflake GES-C01 考古題產品，離你的夢想更近了一步。我們為你提供的 Snowflake GES-C01 題庫資料不僅能幫你鞏固你的專業知識，而且還能保證讓你一次通過 GES-C01 考試。

購買後，立即下載 GES-C01 題庫 (SnowPro® Specialty: Gen AI Certification Exam): 成功付款後, 我們的體統將自動通過電子郵箱將您已購買的產品發送到您的郵箱。（如果在12小時內未收到，請聯繫我們，注意：不要忘記檢查您的垃圾郵件。）

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

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

2. A data engineering team needs to implement a highly accurate, low-latency solution for classifying specialized technical documents into 50 distinct categories. They are considering fine-tuning a Large Language Model (LLM) within Snowflake Cortex for this task. Which of the following considerations are critical for optimizing the fine-tuned model's performance and minimizing inference latency for production use? (Select all that apply)

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

3. A data platform architect is evaluating the integration of 'SNOWFLAKE.CORTEX.TRANSLATE into several automated data pipelines. One pipeline involves real-time translation of messages for a chat application, while another is for batch processing of archived documents. The architect is considering various Snowflake features for orchestration and deployment. Which of the following considerations about 'SNOWFLAKE.CORTEX.TRANSLATE is accurate?

A) The Snowflake managed model used by the 'TRANSLATE' function has a context window of 4,096 tokens, meaning texts longer than this will be truncated before translation.
B) If 'TRANSLAT' is not natively available in the account's primary Snowflake region, cross-region inference cannot be enabled, thus preventing its use.
C) When using 'TRANSLATE within a Snowpark Python User-Defined Function (UDF), the raw text data must be explicitly moved out of Snowflake's network boundary to the underlying LLM service for translation.
D) The 'TRANSLATE' function can be seamlessly integrated into a dynamic table's ' SELECT statement to provide continuous, automated translation with minimal configuration.
E) To manage potential failures in a production pipeline, 'SNOWFLAKCORTEX.TRANSLATE should be wrapped in 'TRY COMPLETE' for robust error handling, returning NULL on failure instead of an error.

4. A development team is creating a new search application using Snowflake Cortex Search. They are currently using a 'snowflake-arctic- embed-I-v2.0' embedding model. After an initial load of 10 million rows, each with approximately 500 tokens of text, they observe a significant 'EMBED_TEXT_TOKENS' cost. They want to minimize these costs for future updates and ongoing operations. Considering their goal to optimize 'EMBED_TEXT_TOKENS' costs, which two strategies should the team prioritize for their Cortex Search Service?

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

5. 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) Configuring the 'base_table' parameter to directly reference a dynamic table, ensuring real-time data ingestion and processing before SQL generation.
B) 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.
C) Providing detailed 'name', 'description' , and 'synonyms' for logical tables, dimensions, and facts to bridge the gap between business terminology and the underlying database schema.
D) Specifying a dedicated 'CORTEX SEARCH SERVICE for every dimension to pre-compute all possible literal values, optimizing response time.
E) Utilizing advanced data types like 'VARIANT' and 'OBJECT for all dimensions to accommodate semi-structured data without complex transformations.

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