[2026] Snowflake SnowPro® Specialty: Gen AI Certification

문제1

A. The CREATE COMPUTE POOL privilege to provision resources for the Streamlit application.

B.

C. The ACCOUNTADMIN role to ensure unrestricted access to all Snowflake Cortex features.

D.

E. The USAGE privilege on the specific database and schema where the Streamlit application and its underlying data tables are located.

정답: B,E

설명: (Fast2test 회원만 볼 수 있음)

문제2

A.

B.

C.

D. Cross-region inference is automatically managed by Snowflake for allowed models, implying that a new, larger virtual warehouse is required to handle the cross- region data transfer overhead.

E.

정답: E

설명: (Fast2test 회원만 볼 수 있음)

문제3

An analytics team is preparing documents for a new Document AI model build to extract information from internal policy reviews. They have a variety of documents that they intend to upload to an internal stage for processing. The document list includes: (1) a 70 MB PDF with 100 pages, (2) a 45 MB DOCX with 150 pages, (3) a 30 MB PNG image, (4) a 60 MB TIFF image, and (5) a 20 MB HTML file. All documents are in English. Which of these documents would 'fail' to meet the direct input requirements for Document AI processing?

A. The 70 MB PDF with 100 pages.

B. The 60 MB TIFF image.

C. The 30 MB PNG image.

D. The 45 MB DOCX with 150 pages.

E. The 20 MB HTML file.

정답: A,B,D

설명: (Fast2test 회원만 볼 수 있음)

문제4

A software development team is building a conversational AI application within Snowflake, aiming to provide a dynamic and stateful chat experience for users. The application needs to handle follow-up questions while maintaining context, provide responses with a degree of creative variation, and actively filter out any potentially harmful content. The team utilizes the SNOWFLAKE. CORTEX. COMPLETE (or AI_COMPLETE) function.

A.

B.

C.

D.

정답: B

설명: (Fast2test 회원만 볼 수 있음)

문제5

A data engineering team is preparing a large corpus of unstructured text documents for a Retrieval Augmented Generation (RAG) application in Snowflake, leveraging Cortex Search and LLM functions. They plan to use SNOWFLAKE.CORTEX.SPLIT_TEXT_RECURSIVE_CHARACTER as part of their data ingestion pipeline. What is the primary benefit of employing this helper function in the context of their RAG workflow?

A. It compresses the text data to reduce storage costs in Snowflake stages before processing by embedding models.

B. It generates vector embeddings for each document chunk, eliminating the need for separate embedding models.

C. It automatically translates documents into a target language, ensuring multilingual compatibility for the LLM.

D. It performs sentiment analysis on each chunk, allowing the RAG system to filter out negative or irrelevant content before retrieval.

E. It divides lengthy documents into smaller, manageable text chunks, which improves the precision of information retrieval and the relevance of downstream LLM responses.

정답: E

설명: (Fast2test 회원만 볼 수 있음)

문제6

A data engineering team is tasked with improving the accuracy of a Cortex Analyst solution for a large e-commerce product catalog. Users frequently ask natural language questions involving specific product names, brands, and categories. The team observes that Cortex Analyst sometimes struggles to identify and correctly filter by these literal values in the generated SQL. Which of the following configurations or approaches, within the semantic model, can effectively enhance Cortex Analyst's ability to precisely identify and use literal values for filtering, based on Snowflake's best practices?

A. Option C

B. Option E

C. Option D

D. Option A

E. Option B

정답: D,E

설명: (Fast2test 회원만 볼 수 있음)

문제7

A data scientist fine-tuned a mistral-lb model in Snowflake for a specific customer support response generation task, naming it my_custom_responder_model. They now want to make this model available for AI_COMPLETE calls in production, ensuring proper access control and regional availability. Which of the following statements is true regarding the deployment and management of this fine-tuned model in Snowflake?

A. Option C

B. Option E

C. Option D

D. Option A

E. Option B

정답: A

설명: (Fast2test 회원만 볼 수 있음)

문제8

An AI development team is deploying a new Cortex Agent and needs to ensure optimal performance and adherence to Snowflake's Gen AI principles regarding data governance. Which of the following statements accurately reflect the models supported by Cortex Agents and the data governance considerations?

A. Option C

B. Option E

C. Option D

D. Option A

E. Option B

정답: A,B,E

설명: (Fast2test 회원만 볼 수 있음)

문제9

An ML engineer is preparing a Docker image for a custom LLM application that will be deployed to Snowpark Container Services (SPCS). The application uses a mix of packages, some commonly found in the Snowflake Anaconda channel and others from general open-source repositories like PyPI. They have the following Docker-file snippet and need to ensure the dependencies are correctly installed for the SPCS environment to support a GPU workload. Which of the following approaches for installing Python packages in the Dockerfile would ensure a robust and compatible setup for a custom LLM running in Snowpark Container Services, based on best practices for managing dependencies in this environment?

A.

B.

C.

D.

E.

정답: A

설명: (Fast2test 회원만 볼 수 있음)

문제10

A business team using a Snowflake Cortex Analyst-powered chatbot reports that follow-up questions in multi-turn conversations are sometimes slow to process, impacting user experience. The development team wants to optimize for responsiveness while maintaining accuracy in SQL generation. Which of the following strategies directly addresses latency in multi-turn conversations within Cortex Analyst, considering its underlying mechanisms?

A. Implement an explicit LLM summarization agent within the semantic model to condense conversation history before it's passed to subsequent LLM calls.

B. Increase the warehouse size used for Cortex Analyst queries to 'Large' to accelerate LLM inference.

C. Configure the semantic model to reset the conversation context after every three turns to limit token count.

D. Switch the underlying text-to-SQL LLM to a smaller model, such as

E. Rely on

정답: A

설명: (Fast2test 회원만 볼 수 있음)

문제11

A.

B.

C.

D.

E.

정답: C,D,E

설명: (Fast2test 회원만 볼 수 있음)

문제12

An ML Engineer has successfully deployed a custom text embedding model, 'my_embedder model', to a Snowpark Container Service named 'text embedding_service' within their Snowflake account. This model has an 'encode' method that accepts a string and returns a vector. They now need to integrate inference calls from this deployed model into various applications. Which of the following are valid ways to invoke this model for inference?

A. Option C

B. Option E

C. Option D

D. Option A

E. Option B

정답: A,D,E

설명: (Fast2test 회원만 볼 수 있음)

문제13

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. The argument must contain exactly two string values for effective binary classification, otherwise an error is returned.

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. If the input text exceeds a model-specific token limit, CLASSIFY_TEXT will automatically truncate the text before processing without raising an error.

정답: A,D

설명: (Fast2test 회원만 볼 수 있음)

Snowflake SnowPro® Specialty: Gen AI Certification - GES-C01무료 덤프문제 풀어보기

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