About Snowflake SnowPro® Specialty: Gen AI Certification exam torrent

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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. 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) 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.
B) 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.
C) 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.
D) 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.
E) 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.

2. A Gen AI engineer is tasked with selecting the most suitable Large Language Model (LLM) from Snowflake Cortex AI for a new customer service chatbot. They need to rapidly prototype and compare different LLMs with varying parameters on a sample dataset before committing to a production deployment. Which of the following statements accurately describe how the Cortex Playground (Public Preview) can assist in this scenario?

A) It provides a mechanism to deploy the chosen LLM directly into Snowpark Container Services (SPCS) compute pools from within the playground for immediate production use.
B) It supports exporting the tested prompts and model configurations as Python code, ready for integration into a Snowpark ML pipeline.
C) It allows connection to a Snowflake table with textual data, processing up to 100 rows, to experiment with prompts directly on actual data.
D) It allows direct fine-tuning of selected LLMs with custom datasets within the playground interface to improve model performance for specific tasks.
E) It enables side-by-side comparison of model outputs for different LLMs and model settings, facilitating an informed decision on model selection.

3. A company is using Snowflake AI Observability to evaluate a summarization application. The application utilizes SNOWFLAKE. CORTEX. COMPLETE for LLM inference and a custom Python component for text pre-processing. The team is particularly interested in tracking detailed cost breakdowns and assessing the factual correctness of the LLM-generated summaries. Which of the following statements accurately describe the cost implications and evaluation metric capabilities in this scenario?

A) The ORTEX_DOCUMENT_PROCESSING_USAGE_HISTORY view is the primary tool to monitor the credit consumption specifically for AI Observability evaluations and LLM judge usage.
B) For evaluating summarization tasks, the 'context relevance' score is the most important metric, as it directly assesses the quality of the LLM's output against the source document.
C) To measure the factual correctness of LLM-generated summaries based on original input and avoid hallucinations, the 'factual correctness' and 'comprehensiveness' metrics can be used during evaluations.
D) AI Observability incurs charges for the LLM judges invoked via COMPLETE (SNOWFLAKE .CORTEX) calls to compute evaluation metrics, in addition to warehouse charges for managing runs and queries.
E) The cost of AI Observability is primarily determined by the number of messages processed, and the number of tokens in each message does not affect the cost, ensuring predictable pricing.

4. A data engineer is integrating a custom application with Snowflake Cortex to leverage the 'COMPLETE' function via its REST API. They are preparing a 'curl' request to send a prompt to the 'mistral-large? model. Which of the following 'curl' command configurations correctly specifies the ''mandatory'' authentication header and a valid token type for accessing the Cortex REST API?

A)

B)

C)

D)

E)

5. A development team is building a RAG application in Snowflake Cortex that needs to extract high-fidelity text and layout from a collection of technical documentation PDFs stored in an internal stage to power semantic search and LLM responses. They want to ensure proper context retrieval for complex user queries. Given this scenario, which of the following actions or statements are crucial for effectively leveraging AI_PARSE_DOCUMENT to optimize the RAG pipeline?

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

Solutions: