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Zertifizierung der GES-C01 mit umfassenden Garantien zu bestehen

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Viele der GES-C01 Fragenkatalog SnowPro® Specialty: Gen AI Certification Examaus PrüfungFrage sind in der Form von Vielfache-Wahl-Fragen. Um Ihre GES-C01 Zertifizierungsprüfungen reibungslos zu meistern, brauchen Sie nur unsere Snowflake GES-C01 Prüfungsfragen und Antworten (SnowPro® Specialty: Gen AI Certification Exam) auswendigzulernen.

Snowflake SnowPro® Specialty: Gen AI Certification Exam GES-C01 Prüfungsfragen mit Lösungen (Q229-Q234):

229. Frage
A new data analyst is trying to incorporate sentiment analysis using SNOWFLAKE. CORTEX. SENTIMENT within a Snowflake data pipeline that uses dynamic tables. They execute the following SQL to create a dynamic table for daily sentiment aggregation:

However, this operation fails. Which of the following is the most direct reason for the failure of this specific setup?

A. The TARGET_LAG for dynamic tables must be explicitly set to '1 day' or longer when integrating with Cortex functions.
B. SNOWFLAKE. CORTEX. SENTIMENT and other Snowflake Cortex functions are currently incompatible with dynamic tables.
C. The warehouse my_analytics_wh is likely not a Snowpark-optimized warehouse, which is a requirement for Cortex functions within dynamic tables.
D. The CORTEX_USER database role was not granted to the analyst's role, preventing the execution of Cortex functions.
E. The review_content column, if containing non-English text, would cause the SENTIMENT function to fail outright rather than produce inaccurate results.

Antwort: B

Begründung:
Option B is correct. Snowflake Cortex functions, including SNOWFLAKE .CORTEX. SENTIMENT, do not support dynamic tables. This is a fundamental limitation that would cause the CREATE DYNAMIC TABLE statement to fail when trying to incorporate a Cortex function. While the 'CORTEX_USER role is indeed required for calling Cortex AI functions, the direct failure in this scenario is due to the incompatibility with dynamic tables. Option C is incorrect as there's no specified TARGET_LAG' limitation. Option D is incorrect; Snowflake recommends using a smaller warehouse (no larger than MEDIUM) for Cortex functions, but a Snowpark-optimized warehouse is not a strict requirement, and larger warehouses do not increase performance. Option E is incorrect because 'SENTIMENT is designed for English-language input text, and non-English text would likely lead to unexpected or inaccurate results, not a direct failure of the function call itself.

230. Frage
A data analyst is working with a table containing customer feedback text and needs to perform various text analysis tasks efficiently within Snowflake. They want to summarize the reviews, determine their sentiment, and extract specific pieces of information. Which of the following Snowflake Cortex LLM functions, when applied to a text column, will achieve the desired outcome and return the specified output type?

A. To extract a specific answer to a question from each review, the analyst can use
B. To get a concise overview of each review, the analyst should use
C. To determine the overall sentiment of each review, the analyst should use
D. The
E. To categorize reviews into predefined labels, the analyst should use

Antwort: B,E

Begründung:
Option A is correct because the 'SUMMARIZE' function takes an English-language input text and returns a string containing a summary of the original text. Option B is incorrect because the 'SENTIMENT function returns a floating-point number from -1 to 1 (inclusive) indicating the level of negative or positive sentiment, not an INTEGER. Option C is incorrect because the 'EXTRACT_ANSWER function returns a string containing an answer to the given question, not a JSON object. Option D is correct because the 'CLASSIFY _ TEXT function classifies free-form text into categories and returns an OBJECT value (VARIANT) with a 'label' field specifying the category. 'AI_CLASSIFY' is the latest version of this function. Option E is incorrect because 'AI_AGG' aggregates a text column and returns insights across multiple rows based on a user-defined prompt, and importantly, it is not subject to context window limitations.

231. Frage
A data architect is designing a workflow to programmatically extract highly structured data from various text inputs using Snowflake Cortex AI's AI_COPIPLETE function through its REST API. They require the output to strictly adhere to a complex JSON schema for downstream processing and need to manage associated costs. Which of the following statements accurately describe aspects of this approach?

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

Antwort: B,C,D

Begründung:

232. Frage
A financial services company uses Snowflake Cortex's AI_COMPLETE for sentiment analysis on customer call transcripts, which contain personally identifiable information (PII). They also fine-tune a llama3.1-70b model with proprietary financial data. Which of the following statements accurately describe Snowflake's Gen AI principles regarding data privacy, model usage, and governance in this scenario?

A. The fine-tuned llama3.1-70b model, including the proprietary training data used, is exclusively owned by the financial institution and is not shared with other Snowflake customers.
B. Enabling Cortex Guard for AI_COMPLETE automatically anonymizes all PII within the prompt before it reaches the LLM, regardless of the model chosen, to ensure privacy.
C. When using Cortex Analyst with Snowflake-hosted LLMs, metadata and prompts are transmitted outside Snowflake's governance boundary for processing, incurring additional cross-cloud data transfer costs.
D. Customer Data (inputs and outputs) for AI_COMPLETE, including PII, are guaranteed not to be available to other customers or used to train models made available to others.
E. Snowflake's metadata fields, such as table and column names, should not contain personal, sensitive, or export-controlled data when using Snowflake AI services, to maintain data governance.

Antwort: A,D,E

Begründung:
Option A is correct because Snowflake's trust and safety principles explicitly state that Usage and Customer Data (inputs and outputs of Snowflake AI Features) are NOT available to other customers and are NOT used to train, re-train, or fine-tune Models made available to others. Option B is correct because fine-tuned models built using your data can only be used by you, ensuring exclusivity. Option C is correct as customers should ensure no personal, sensitive, or regulated data is entered as metadata when using the Snowflake service. Option D is incorrect because Cortex Guard filters unsafe and harmful responses from the LLM; it does not automatically anonymize PII in the prompt before it reaches the LLM. Option E is incorrect because when Cortex Analyst is powered by Snowflake-hosted LLMs, data (including metadata or prompts) stays within Snowflake's governance boundary.

233. Frage
An AI developer is building a Snowflake data pipeline to prepare unstructured data for a RAG application. The pipeline involves extracting text, splitting it into chunks, generating embeddings, and then indexing for Cortex Search. Considering the role of helper functions like SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER
, which of the following statements accurately describes its typical operational placement and interaction within this Gen AI pipeline?

A. It is a post-processing step for LLM-generated responses, used to break down long answers into digestible paragraphs for user display in chat interfaces.
B. It replaces the need for
C. It is typically applied after an embedding function (e.g.,
D. The function's recursive nature enables it to automatically detect and correct factual inconsistencies or 'hallucinations' present in the original large text documents before they are embedded.
E. Its output, consisting of smaller text chunks, serves as the direct input for text embedding functions that then convert these chunks into vector representations for semantic indexing.

Antwort: E

Begründung:
Option B is correct.

is a helper function used to divide large text documents into smaller chunks. These smaller text chunks are then processed by embedding functions, such as

, to create vector embeddings that are subsequently used for indexing and semantic search in RAG applications. Option A is incorrect because text splitting (chunking) happens *before* embedding generation, not after, as it prepares the raw text for vectorization. Option C is incorrect; COUNT_TOKENS is a separate helper function specifically designed to return the token count of input text. SPLIT_TEXT_RECURSIVE_CHARACTER does not implicitly provide token counts for its output chunks. Option D is incorrect; the function's purpose is text splitting, not hallucination detection or correction, which pertains to LLM output quality. Option E is incorrect; while LLM responses might be formatted, the primary role of SPLIT_TEXT_RECURSIVE_CHARACTER is in preparing input documents for RAG, not post-processing LLM outputs for display.

234. Frage
......

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