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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) Cortex REST API rate limits are primarily based on Requests per Minute (RPM) and do not consider the total Tokens Processed per Minute (TPM).
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) 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.
D) 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.
E) 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.

2. A financial analytics team is using to extract specific financial metrics (e.g., revenue, profit margin) from quarterly reports and requires the output in a strict JSON format for automated ingestion into a data warehouse. They've encountered issues where the LLM sometimes generates malformed JSON or includes extraneous text. Which of the following approaches will help ensure deterministic, schema-compliant JSON outputs and mitigate these 'hallucinations' related to format?

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

3. A developer is refining a Document AI extraction process using the '!PREDICT' method and is meticulously examining the JSON output for invoices, which include 'invoice number', 'invoice items', 'tax amount', and 'vendor name'. They also have a detailed internal table of 'product details' to be extracted. To ensure optimal data quality and accurate interpretation of the extracted information, which of the following best practices or characteristics of Document AI's output should the developer consider?

A) The 'ocrScore' provided in the '_documentMetadata' object for each document indicates the model's confidence in the content of specific extracted values, rather than the overall quality of the optical character recognition process.
B) For table extraction, such as the extracted values for each column (e.g., 'tablel litem', 'tablel Igross) are ordered consistently with the rows of the original table, facilitating direct joining of columns.
C) If the 'vendor_name' field cannot be confidently identified in a document, the model will include '"vendor_name": [ { "score": O.X, "value": "NOT FOUND" } l' in the JSON output.
D) When extracting lists of values, such as 'invoice_items', the Document AI model returns them as an array in the JSON output, preserving the original order of items as they appear in the document.
E) To maximize accuracy when defining data values, questions should be broadly generic (e.g., 'What is the amount?) to allow the Document AI model to infer the most relevant context, especially for fields like 'tax_amount' where multiple numbers might be present.

4. 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) 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.
B) The Cortex Search Service can effectively be used as a RAG engine for LLM chatbots by leveraging semantic search capabilities to provide customized and contextualized responses from the text data.
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 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.

5. A compliance officer is reviewing the usage of Snowflake Cortex LLM functions and the Cortex REST API within their organization, specifically focusing on the implementation and impact of Cortex Guard. They observe several instances where 'guardrails' were enabled. Which of the following statements accurately describe the behavior and cost considerations of Cortex Guard when integrated with Snowflake Cortex LLM functions or the Cortex REST API?

A) Cortex Guard operates by evaluating responses after the LLM has fully generated its content, and it incurs additional compute cost for both input and output tokens during its processing.
B) Cortex Guard is inherently part of all Cortex LLM functions and does not require explicit enablement via 'guardrails: TRUE for SQL functions or the REST API.
C) The underlying model for Cortex Guard is Meta's Llama Guard 3, and its processing costs are separate from the primary LLM inference costs.
D) When Cortex Guard is enabled and a response is deemed unsafe, the LLM-generated output is replaced with a predefined message, and only the input tokens for Cortex Guard processing ('guard_tokens') are billed, not the potentially unsafe completion tokens.
E) Cortex Guard can be configured with a custom message using the argument in the options object for both 'COMPLETE SQL function and the Cortex REST API.

Solutions: