Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1. A company is building a chatbot for internal support, powered by Snowflake Cortex LLMs. The primary goals are to provide answers that are accurate, grounded in proprietary documentation, and to minimize factual 'hallucinations'. They are considering various strategies to achieve this. Which of the following statements correctly describe effective methods or tools within Snowflake for addressing these concerns?

A) AI Observability can be leveraged to systematically evaluate applications, measuring metrics like 'factual correctnesS and 'groundedness' to detect and mitigate hallucinations, especially in summarization.
B) Using Cortex Search as a Retrieval Augmented Generation (RAG) engine can enhance LLM responses by providing relevant context from proprietary documentation, thereby reducing hallucinations.
C) Enabling Cortex Guard with guardrails: true directly addresses model hallucinations by ensuring responses are always factually correct and aligned with the provided context.
D) Deploying a custom fine-tuned model using SNOWFLAKE. CORTEX. FINETUNE on proprietary documentation is the most effective approach to ensure factual accuracy for any LLM task.
E) For tasks requiring LLMs to generate SQL queries from natural language, using the can improve accuracy by Cortex Analyst Verified Query Repository (VQR) leveraging pre-verified SQL queries for similar questions.

2. A data engineer is building a robust pipeline to process customer feedback. They need to extract specific sentiment categories (food_quality, food_taste, wait_time, food _cost) from text reviews and ensure the output is always a valid JSON object matching a predefined schema, even for complex reviews. They also want to control the determinism of the LLM responses. Which of the following SQL statements or considerations are correct for achieving this using Snowflake Cortex AI functions?

A) The response_format argument with a JSON schema is primarily for OpenAl (GPT) models; for other models like Mistral, a strong prompt instruction such as 'Respond in strict JSON' is generally more effective.
B) Using AI_COMPLETE with response_format incurs additional compute cost for the overhead of verifying each token against the supplied JSON schema, in addition to standard token costs.
C) To ensure the model explicitly attempts to extract all specified fields, the 'required' array in the JSON schema is critical; AI_COMPLETE will raise an error if any required field cannot be extracted.
D) The following SQL statement uses the response_format argument and temperature setting to achieve structured output and determinism:

E) For the most consistent structured output, especially in complex reasoning tasks, setting the temperature option to 0 when calling AI_COMPLETE is recommended.

3. A financial institution uses Snowflake Cortex Analyst with strict role-based access control (RBAC) on their Snowflake-hosted LLMs. The security team has granted specific 'CORTEX-MODEL-ROLE application roles to different analyst teams, ensuring they only access approved models. A new requirement arises to enable Azure OpenAI GPT models for Cortex Analyst to leverage a specific feature. An administrator proceeds to execute:

Which of the following statements accurately describe the implications of this change?

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

4. 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 D
B) Option B
C) Option E
D) Option C
E) Option A

5. A data team has implemented a Snowflake data pipeline using SQL tasks that process customer call transcripts daily. This pipeline relies heavily on SNOWFLAKE. CORTEX. COMPLETE() (or its updated alias) for various text analysis tasks, such as sentiment analysis and summary generation. Over time, they observe that the pipeline occasionally fails due to LLM-related errors, and the compute costs are higher than anticipated. What actions should the team take to improve the robustness and cost-efficiency of this data pipeline? (Select all that apply.)

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

Solutions: