Snowflake SnowPro® Specialty: Gen AI Certification

문제1
A data application developer, adhering to Snowflake's Gen AI best practices for deploying LLMs, needs to perform inference with a newly fine-tuned llama3.1-70b model via AI_COMPLETE and expects a structured JSON output. Which of the following statements accurately describe how to configure this inference and potential limitations within Snowflake Cortex?

A. Option E

B. Option B

C. Option A

D. Option D

E. Option C

정답: B,D

설명: (KoreaDumps 회원만 볼 수 있음)

문제2
A data engineering team is tasked with creating vector embeddings for a collection of diverse, multilingual research papers for a semantic search application. They need to use 'SNOWFLAKE.CORTEX.EMBED TEXT 1024' and are considering two models: 'snowflake-arctic-embed-l-v2.0' and 'voyage-multilingual-2'. They also need to ensure the resulting embeddings are stored correctly and understand potential text truncation. Which of the following statements correctly describes the application of the 'EMBED TEXT 1024' function for these models and the characteristics of the generated embeddings?

A. The function call

B. The query

C. Both 'snowflake-arctic-embed-l-v2.0' and 'nv-embed-qa-4' are multilingual models with a 1024-dimension output, making them interchangeable for this use case.

D. The 'snowflake-arctic-embed-I-v2.0' model has a larger context window than 'voyage-multilingual-2 , making it more suitable for longer research papers without truncation.

E. Using either 'snowflake-arctic-embed-l-v2.0' or 'voyage-multilingual-2' with will result in a 'VECTOR(FLOAT, 1024)' data type, which is compatible with 'VARIANT columns.

정답: B

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문제3
A Snowflake developer, 'AI _ ENGINEER , is creating a Streamlit in Snowflake (SiS) application that will utilize a range of Snowflake Cortex LLM functions, including SNOWFLAKE. CORTEX. COMPLETE, SNOWFLAKE .CORTEX.CLASSIFY TEXT, and SNOWFLAKE. CORTEX. EMBED TEXT 768. The application also needs to access data from tables within a specific database and schem a. 'AI _ ENGINEER has created a custom role, for the application to operate under. Which of the following privileges or roles are absolutely necessary to grant to for the successful execution of these Cortex LLM functions and interaction with the specified database objects? (Select all that apply.)

A.

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

C.

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

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

정답: A,B

설명: (KoreaDumps 회원만 볼 수 있음)

문제4
A data engineer is establishing a new Snowflake environment to support Document AI for processing incoming vendor invoices. They are setting up the necessary virtual warehouse, database, schema, and stages. Which of the following statements correctly identify essential considerations or requirements for this initial setup?

A. A dedicated, smaller warehouse (e.g., 'X-SMALL', 'SMALL', or 'MEDIUM') should be created for Document AI to facilitate precise cost tracking, as scaling up warehouse size does not enhance Document AI query performance.

B. Snowflake recommends using a large virtual warehouse, such as an 'L' or 'XL' size, to accommodate the intensive processing demands of Document AI and ensure high throughput.

C. Any internal stages used for storing documents that will be processed by Document AI must explicitly enable SNOWFLAKE_SSE encryption.

D. All documents intended for a single Document AI '!PREDICT operation must be stored within the same logical directory of the specified stage.

E. The database and schema where Document AI model builds are created can be freely altered after creation, allowing for agile schema evolution.

정답: A,C,D

설명: (KoreaDumps 회원만 볼 수 있음)

문제5
A data architect is integrating Snowflake Cortex LLM functions into various data enrichment pipelines. To ensure optimal performance, cost-efficiency, and accuracy, which of the following are valid best practices or considerations for these pipelines?

A. When performing sentiment analysis on customer feedback using 'AI_SENTIMENT, it's best practice to pass detailed, multi-turn conversation history to the function to enhance accuracy, similar to how 'AI_COMPLETE handles conversational context.

B. For data enrichment involving classification with 'AI_CLASSIFY' , using descriptive and mutually exclusive categories in plain English, along with an optional clear task description, can significantly improve classification accuracy.

C. To manage costs effectively for LLM functions like SAI COMPLETE in a pipeline, always use the largest available warehouse size (e.g., 6XL Snowpark- optimized) to maximize throughput, as this directly reduces the overall token processing time and cost.

D. When extracting specific entities from documents using SAI EXTRACT or '!PREDICT , it is often more effective to fine-tune a Document AI model for complex or varied document layouts rather than relying solely on extensive prompt engineering for zero-shot extraction.

E. For tasks requiring deterministic JSON outputs, explicitly specifying a JSON schema using the 'response_format' argument with 'AI COMPLETE is crucial, and for OpenAI (GPT) models, including the 'required' field and setting 'additionalPropertieS to 'false' in every node of the schema is a mandatory requirement.

정답: B,D,E

설명: (KoreaDumps 회원만 볼 수 있음)

문제6
A Gen AI Specialist in Snowflake Cortex aims to fine-tune an LLM for enhanced task-specific performance. When creating a fine-tuning job using SNOWFLAKE. CORTEX. FINETUNE( 'CREATE', ... ) , which statement accurately describes the required training data format and a supported base model, aligning with Snowflake's Gen AI principles for leveraging LLMs?

A. Option E

B. Option B

C. Option A

D. Option D

E. Option C

정답: B

설명: (KoreaDumps 회원만 볼 수 있음)

문제7
A data scientist is working on a new feature that involves querying a Cortex Search Service and integrating the results into various downstream processes. They are concerned about potential data type compatibility issues and limitations within the Snowflake environment. Which of the following statements accurately describe how to query a Cortex Search Service or the limitations of the 'VECTOR data type and Cortex Search itself? (Select all that apply)

A. The 'VECTOR data type, which stores the output of embedding models like those used by Cortex Search, is explicitly not supported in 'VARIANT columns.

B. Cortex Search Services can only be queried using the 'SNOWFLAKE.CORTEX.SEARCH_PREVIEW' SQL function and do not offer a programmatic interface for applications.

C. The "VECTOR data type, used to store embeddings generated for Cortex Search, is fully supported as a primary key in Snowflake's hybrid tables to accelerate similarity searches.

D. Cortex Search Services can be configured as a source for Snowflake dynamic tables, enabling continuous and automated synchronization of the search index with base data.

E. To retrieve the support ticket most relevant to a query about 'internet issues', filtered to return results only in the 'North America' region, one can use the 'filter' parameter in the 'SEARCH PREVIEW function's JSON argument like so:

정답: A,E

설명: (KoreaDumps 회원만 볼 수 있음)

문제8
A security auditor needs to access and analyze logs generated by Snowflake AI Observability for compliance auditing and to track the activity of generative AI applications. They need to understand how to reliably query this data and its temporal characteristics within Snowflake. Which of the following statements accurately describes the access and characteristics of this logged data?

A. Logged data from AI Observability's event tables becomes visible within a small latency, typically 1-2 minutes, after a request is made.

B. Logs are exclusively available for analysis through pre-built dashboards in Snowsight and cannot be accessed via direct SQL queries.

C. Access to these detailed event tables is implicitly granted to roles holding the SNOWFLAKE. CORTEX_USER database role and the AI_OBSERVABILITY_EVENTS_LOOKUP application role.

D. Detailed request and response bodies, along with the generated SQL, are stored and can be directly queried using standard SQL.

E. The logs are automatically purged after 7 days of being recorded, requiring a separate process for long-term data retention.

정답: A,C,D

설명: (KoreaDumps 회원만 볼 수 있음)

문제9
A machine learning engineering team is evaluating two different configurations of a Retrieval Augmented Generation (RAG) application. uses for generation, while uses 'mistral-7b' with a refined prompt for the same task. They aim to compare the and 'groundedness' of the generated responses, as well as the efficiency of context retrieval. Which of the following steps are crucial for setting up AI Observability in Snowflake to facilitate a meaningful side-by-side comparison and assess these specific metrics?

A. Option E

B. Option B

C. Option A

D. Option D

E. Option C

정답: C,D,E

설명: (KoreaDumps 회원만 볼 수 있음)

문제10
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 B

C. Option A

D. Option D

E. Option C

정답: A,B,E

설명: (KoreaDumps 회원만 볼 수 있음)

문제11

A.

B.

C.

D.

E.

정답: A,B,C,D

설명: (KoreaDumps 회원만 볼 수 있음)

문제12
A financial institution needs to process thousands of incoming PDF loan application forms daily, extracting applicant names, loan amounts, and submission dates, and loading them into a Snowflake table. They aim for continuous processing with minimal manual intervention. Which of the following statements correctly describe how Document AI can be used in an automated SQL pipeline for this purpose?

A. To ensure continuous data ingestion and processing, a STREAM can be created on the stage to detect new PDF documents, triggering the TASK for extraction and subsequent loading into a Snowflake table.

B. The extracted information, including confidence scores and values, is returned as a JSON object, which can then be parsed into separate columns in a Snowflake table using SQL functions like LATERAL FLATTEN.

C. Document AI's PREDICT method natively supports all PDF files up to 500 MB and 500 pages, allowing for large-scale, single-query processing without requiring users to split documents into smaller chunks.

D. The pipeline can leverage the <model build name> ! PREDICT method within a CREATE TASK statement to automatically process new PDFs as they arrive in an internal or external stage, once the Document AI model build is published.

E. The SNOWFLAKE .DOCUMENT_INTELLIGENCE_CREATOR database role alone is sufficient for defining the model build and configuring the processing pipeline, without needing additional CREATE MODEL privileges on the schema.

정답: A,B,D

설명: (KoreaDumps 회원만 볼 수 있음)

문제13
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 A

D. Option D

E. Option C

정답: B,C

설명: (KoreaDumps 회원만 볼 수 있음)

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