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Snowflake GES-C01 Exam Syllabus Topics:

Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) 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.
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) 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.

2. A data engineering team is building an automated pipeline in Snowflake to process incoming sensor dat a. Each sensor reading includes a 1024-dimensional feature vector, and the team needs to flag readings that are significantly different from a baseline reference vector using VECTOR_L1_DISTANCE
. The pipeline uses Snowflake tasks to orchestrate data loading and transformation. Which statement regarding the integration and operational aspects of this pipeline is true?

A) Snowflake Cortex AI SQL functions, including
B) To ensure efficient processing of new sensor data, the
C) For optimal performance when calculating
D) The
E) The

3. An ML engineer is designing a Cortex Agent to provide highly accurate and contextualized responses. They intend for the agent to use state-of-the-art LLMs for orchestration and to maintain a specific brand tone in its outputs. Considering the available models and configurations for Cortex Agents, which statement is true?

A) Cortex Agents are restricted to using only Snowflake Arctic models for orchestration, due to security and governance requirements, and must operate in the account's default region.
B) When an agent utilizes an LLM for orchestration, the system ensures that cross-region inference is automatically enabled without any latency implications, making region selection irrelevant.
C) The agent's 'Planning' component is specifically responsible for evaluating results after each tool use and deciding the subsequent steps in the query resolution process, acting as a feedback loop.
D) To ensure the agent's responses adhere to a desired brand and tone, 'Response instructions' can be configured, which guide the agent's output style and persona.
E) Cortex Agents primarily interact with data through the Snowflake Model Registry API to retrieve and update fine-tuned model parameters during their iterative planning phase.

4. A data team is refining their Cortex Analyst semantic model to improve the accuracy of responses for specific, frequently asked questions and to enable better literal value searches. Consider a semantic model being developed to address these requirements. Which two configurations or features are directly relevant and correctly applied in the semantic model YAML for these purposes?

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

5. A financial analytics team is using AI_COMPLETE 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) Set the 'temperature' option to 0 when calling 'AI_COMPLETE' to obtain the most consistent and deterministic JSON outputs.
B)

C) Include a prompt instruction such as 'Respond in JSON' to improve adherence, especially for complex tasks, even though 'response_format' is provided.
D)

E)

Solutions: