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Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q92-Q97):

NEW QUESTION # 92
A data engineering team is setting up a Retrieval Augmented Generation (RAG) application using Snowflake Cortex Search to provide contextual answers from customer support transcripts. The transcripts are stored in a Snowflake table named SUPPORT_TRANSCRIPTS. Which of the following statements are crucial considerations or accurate facts regarding the initial setup and configuration of the Cortex Search Service for this use case?

A. Cortex Search is designed to get users up and running quickly with a hybrid (vector and keyword) search engine on text data, handling embedding, infrastructure maintenance, and search quality parameter tuning automatically.
B. Snowflake recommends using a dedicated virtual warehouse of any size, including X-Large or 2X-Large, for each Cortex Search Service to ensure the fastest possible materialization of search indexes during creation and refresh.
C. The CREATE CORTEX SEARCH SERVICE command requires that CHANGE_TRACKING = TRUE be enabled on the source table, especially if the role creating the service is not the table owner. This ensures that the service can track updates to the base data.
D. Columns specified in the ATTRIBUTES field during service creation are only used for filtering search results and do not need to be present in the source query.
E. Cortex Search Services currently support replication and cloning, allowing for easy disaster recovery and geographical distribution of the search index.

Answer: A,C

Explanation:
Option A is correct because change tracking is required for the Cortex Search Service to monitor updates to the base table, particularly if the service creator is not the table owner. Option B is incorrect; Snowflake recommends using a dedicated warehouse no larger than MEDIUM for each service, as larger warehouses do not necessarily increase performance for index materialization. Option C is incorrect because columns in the ATTRIBUTES field must be included in the source query. Option D is correct as Cortex Search provides a low-latency, high-quality hybrid (vector and keyword) search engine that automatically manages embedding, infrastructure, and search quality tuning. Option E is incorrect because Cortex Search Services currently do not support replication or cloning.

NEW QUESTION # 93
A data scientist is tasked with improving the accuracy of an LLM-powered chatbot that answers user questions based on internal company documents stored in Snowflake. They decide to implement a Retrieval Augmented Generation (RAG) architecture using Snowflake Cortex Search. Which of the following statements correctly describe the features and considerations when leveraging Snowflake Cortex Search for this RAG application?

A. To create a Cortex Search Service, one must explicitly specify an embedding model and manually manage its underlying infrastructure, similar to deploying a custom model via Snowpark Container Services.
B. For optimal search results with Cortex Search, source text should be pre-split into chunks of no more than 512 tokens, even when using models with larger context windows like
C. Enabling change tracking on the source table for the Cortex Search Service is optional; the service will still refresh automatically even if change tracking is disabled.
D. Cortex Search automatically handles text chunking and embedding generation for the source data, eliminating the need for manual ETL processes for these steps.
E. The

Answer: B,D,E

Explanation:
Option A is correct because Cortex Search is a fully managed service that gets users started with a hybrid (vector and keyword) search engine on text data in minutes, without needing to worry about embedding, infrastructure maintenance, or index refreshes. Option B is incorrect because Cortex Search is a fully managed service; users do not need to manually manage the embedding model infrastructure. A default embedding model is used if not specified. Option C is correct because, for best search results with Cortex Search, Snowflake recommends splitting text into chunks of no more than 512 tokens, as smaller chunks typically lead to higher retrieval and downstream LLM response quality, even with models that have larger context windows. Option D is correct because the 'SNOWFLAKE.CORTEX.SEARCH_PREVIEW' function allows users to test the search service to confirm it is populated with data and serving reasonable results for a given query. Option E is incorrect because change tracking is required on the source table for the Cortex Search Service to function correctly and reflect updates to the base data.

NEW QUESTION # 94

After resolving this, they try to process a batch of 1500 documents in a single query using the method, which also fails. Which two issues are most likely contributing to these failures?

A. The 'SNOWFLAKE.DOCUMENT INTELLIGENCE CREATOR database role or the 'CREATE SNOWFLAKE.ML.DOCUMENT INTELLIGENCE' privilege on the schema has not been granted to the .
B. The has not been granted the 'CREATE STREAM' privilege on the
C. The name is not unique within the .
D. The batch of documents exceeds Document AI's limit of 1000 documents per query.
E. The virtual warehouse being used for Document AI is not a Snowpark-optimized warehouse.

Answer: A,D

Explanation:
The initial error, 'Unable to create a build on the specified database and schema,' is a documented message that can occur if the 'CREATE SNOWFLAKE.ML.DOCUMENT_INTELLIGENCE' privilege is not granted to the role, or if the database role is missing. Thus, option B is a direct and likely cause for the first failure. After resolving this (meaning the model build was successfully created), the subsequent failure to process 1500 documents in a single query is due to Document AI's limitation of processing a maximum of 1000 documents per query. Therefore, option D correctly identifies the second contributing factor. Option A (CREATE STREAM' privilege) is relevant for creating pipelines but not for the initial model build or predicting. Option C (duplicate model build name) is another possible cause for the first error, but the question asks for the 'most likely' issues across two distinct failures, making B a more fundamental access control issue for creation and D a clear limit for processing. Option E (Snowpark-optimized warehouse) is not a specific requirement or a known cause for these types of errors; an X-Small, Small, or Medium warehouse is generally recommended, and scaling up does not increase processing speed.

NEW QUESTION # 95
A developer is building a prototype Gen AI application using the Snowflake Cortex LLM REST API. During testing, they frequently encounter HTTP 429 'Too Many Requests' errors, particularly when rapidly invoking the 'COMPLETE endpoint. Which of the following statements accurately describes the usage quotas and troubleshooting steps for such rate limit events in the Cortex REST API?

A. The HTTP 429 response indicates that the daily credit consumption for Snowflake Cortex AISQL usage has exceeded the 10-credit limit for on-demand accounts without a valid payment method, requiring conversion to a paid account.
B. To resolve rate limiting, the developer should increase the virtual warehouse size for their API requests, as larger warehouses automatically increase API quotas.
C. If the usage is below the RPM limit but still triggers a 429, the developer should review the token usage rate as TPM limits might be exceeded.
D. Rate limits are model-specific, and exceeding either the Tokens Processed per Minute (TPM) or Requests per Minute (RPM) limits will result in an HTTP 429 response code.
E. Cortex REST API rate limits are primarily based on Requests per Minute (RPM) and do not consider the total Tokens Processed per Minute (TPM).

Answer: C,D

Explanation:
Option C is correct because the Cortex REST API implements rate limits for both Tokens Processed per Minute (TPM) and Requests per Minute (RPM), and offending either of these limits will result in a 429 response code. Option D is correct as the sources explicitly state that if REST API usage is below the RPM limit but still receives a 429, the user should double-check the token usage rate, implying that TPM limits could be the cause. Option A is incorrect because while on-demand Snowflake accounts without a valid payment method are limited to 10 credits per day, an HTTP 429 specifically refers to rate limiting due to high usage, not a daily credit budget exceeding for the account type. Option B is incorrect as Cortex REST API rate limits consider both TPM and RPM. Option E is incorrect because Cortex REST API requests do not require a warehouse, and increasing warehouse size does not directly increase API quotas or resolve rate limiting for the REST API.

NEW QUESTION # 96
A data engineering team is implementing a Document AI solution to automate the extraction of vendor invoice details. They have already published a Document AI model build, 'vendor_invoice_model', located in New invoices are uploaded to an internal stage, 'fin db.invoice_processing_schema.raw invoices_stage'. The 'invoice_pipeline role' is responsible for executing the
! PREDICT
method on these staged documents as part of an automated task. Which of the following USAGE privileges are essential for to successfully execute the
! PREDICT
method and process documents from the specified stage, assuming the required SNOWFLAKE.DOCUMENT_INTELLIGENCE_CREATOR role is already granted?

Answer: A,C,E

Explanation:

NEW QUESTION # 97
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Test GES-C01 Answers, GES-C01 Passleader Review

GES-C01 Passleader Review | GES-C01 Latest Exam Pass4sure

Snowflake SnowPro® Specialty: Gen AI Certification Exam Sample Questions (Q92-Q97):

Free PDF Quiz 2025 Snowflake GES-C01 – Valid Test Answers

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