Databricks Updated Databricks-Generative-AI-Engineer-Associate Exam Questions and Answers by ayzel

The task is to prevent toxic or unsafe responses in an LLM system using the Foundation Model API with minimal effort. Let’s assess the options.

Option A: Host Llama Guard on Foundation Model API and use it to detect unsafe responses

Llama Guard is a safety-focused model designed to detect toxic or unsafe content. Hosting it via the Foundation Model API (a Databricks service) integrates seamlessly with the existing system, requiring minimal setup (just deployment and a check step), and leverages provisioned throughput for performance.

Databricks Reference:"Foundation Model API supports hosting safety models like Llama Guard to filter outputs efficiently"("Foundation Model API Documentation," 2023).

Option B: Add some LLM calls to their chain to detect unsafe content before returning text

Using additional LLM calls (e.g., prompting an LLM to classify toxicity) increases latency, complexity, and effort (crafting prompts, chaining logic), and lacks the specificity of a dedicated safety model.

Databricks Reference:"Ad-hoc LLM checks are less efficient than purpose-built safety solutions"("Building LLM Applications with Databricks").

Option C: Add a regex expression on inputs and outputs to detect unsafe responses

Regex can catch simple patterns (e.g., profanity) but fails for nuanced toxicity (e.g., sarcasm, context-dependent harm), requiring significant manual effort to maintain and update rules.

Databricks Reference:"Regex-based filtering is limited for complex safety needs"("Generative AI Cookbook").

Option D: Ask users to report unsafe responses

User reporting is reactive, not preventive, and places burden on users rather than the system. It doesn’t limit unsafe outputs proactively and requires additional effort for feedback handling.

Databricks Reference:"Proactive guardrails are preferred over user-driven monitoring"("Databricks Generative AI Engineer Guide").

Conclusion: Option A (Llama Guard on Foundation Model API) is the least-effort, most effective approach, leveraging Databricks’ infrastructure for seamless safety integration.

When addressing concerns of hallucination and data leakage in an LLM application for internal company policies, fine-tuning the model on internal data with the hope it learns data boundaries can be problematic:

Risk of Data Leakage: Fine-tuning on sensitive or confidential data does not guarantee that the model will not inadvertently include or reference this data in its outputs. There’s a risk of overfitting to the specific data details, which might lead to unintended leakage.

Hallucination: Fine-tuning does not necessarily mitigate the model's tendency to hallucinate; in fact, it might exacerbate it if the training data is not comprehensive or representative of all potential queries.

Better Approaches:

A,C, andDinvolve setting up operational safeguards and constraints that directly address data leakage and ensure responses are aligned with specific user needs and security levels.

Fine-tuning lacks the targeted control needed for such sensitive applications and can introduce new risks, making it an unsuitable approach in this context.

Problem Context: The engineer needs an open-source LLM with a large context window to develop an application.

Explanation of Options:

Option A: DistilBERT: While an efficient and smaller version of BERT, DistilBERT does not provide a particularly large context window.

Option B: MPT-30B: This model, while large, is not specified as being particularly notable for its context window capabilities.

Option C: Llama2-70B: Known for its large model size and extensive capabilities, including a large context window. It is also available as an open-source model, making it ideal for applications requiring extensive contextual understanding.

Option D: DBRX: This is not a recognized standard model in the context of large language models with extensive context windows.

Thus,Option C(Llama2-70B) is the best fit as it meets the criteria of having a large context window and being available for open-source use, suitable for developing robust language understanding applications.

Problem Context: When using data to train a model, it’s essential to ensure compliance with licensing to avoid legal risks. Legal issues can arise from using data without permission, especially when it comes from third-party sources.

Explanation of Options:

Option A: Reaching out to data curatorsbeforeusing the data is an appropriate action. This allows you to ensure you have permission or understand the licensing terms before starting to use the data in your model.

Option B: Usingoriginal datathat you personally created is always a safe option. Since you have full ownership over the data, there are no legal risks, as you control the licensing.

Option C: Using data that is explicitly labeled with an open license and adhering to the license terms is a correct and recommended approach. This ensures compliance with legal requirements.

Option D: Reaching out to the data curatorsafteryou have already started using the trained model isnot appropriate. If you’ve already used the data without understanding its licensing terms, you may have already violated the terms of use, which could lead to legal complications. It’s essential to clarify the licensing termsbeforeusing the data, not after.

Thus,Option Dis not appropriate because it could expose you to legal risks by using the data without first obtaining the proper licensing permissions.