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Snowflake SnowPro® Specialty: Gen AI Certification Sample Questions:

1.

A)

B) Data for all these operations remains within Snowflake's governance boundary.
C)

D)

E)

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

3. An ML Engineer deploys a custom fine-tuned LLM on Snowpark Container Services (SPCS) to process multiple independent text streams, generating structured output. The team observes that some streams fail to process, leading to incomplete results, and they also want to effectively monitor the cost of their deployment. Which actions are appropriate for troubleshooting and cost management in this scenario?

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

4. An organization operating in the AWS US West 2 (Oregon) region needs to process sensitive customer support tickets using Snowflake Cortex LLM functions. Due to the diverse availability of specific LLMs, they are considering enabling CORTEX_ENABLED_CROSS_REGION. What is a key data safety and security consideration when enabling CORTEX_ENABLED_CROSS_REGION for Snowflake Cortex LLM functions, specifically regarding data storage and persistence?

A) User inputs and service-generated prompts will be stored in a cache in the remote region to optimize subsequent requests.
B) Data transmitted across regions for inference is encrypted by default, but the encryption keys are managed by the third-party cloud provider in the remote region.
C) It may lead to increased compute costs if the cross-region model is more expensive, but data movement guarantees remain unchanged.
D)

E) It enables inference for features not supported in the local region by allowing data to be processed in a different Snowflake region, and user inputs, prompts, and outputs are not stored or cached.

5. A Gen AI specialist is preparing to upload a large volume of diverse documents to an internal stage for Document AI processing. The objective is to extract detailed information, including lists of items and potentially classifying document types, and then automate this process. Which of the following statements represent 'best practices or important considerations/limitations' when preparing documents and setting up the Document AI workflow in Snowflake? (Select ALL that apply.)

A) When defining data values for extraction, especially for nonstandard formats or combinations of values, fine-tuning the model with annotations is generally more effective than relying solely on complex prompt engineering.
B) If the Document AI model does not find an answer for a specific field, the '!PREDICT method will omit the 'value' key but will still return a 'score' key to indicate confidence that the answer is not present.
C) Documents with a page count exceeding 125 pages or a file size greater than 50 MB will be processed, but with a potential reduction in extraction accuracy.
D) For continuous processing of new documents, it is best practice to create a stream on the internal stage and a task to automate the '!PREDICT method execution.
E) To improve model training, documents uploaded should represent a real use case, and the dataset should consist of diverse documents in terms of both layout and data.

Solutions: