Databricks Databricks-Generative-AI-Engineer-Associate Exam With Confidence Using Practice Dumps

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.

In this scenario, the chatbot must avoid answering political questions and instead provide a standard message for such inquiries. Implementing aSafety Guardrailis the appropriate solution for this:

What is a Safety Guardrail?Safety guardrails are mechanisms implemented in Generative AI systems to ensure the model behaves within specific bounds. In this case, it ensures the chatbot does not answer politically sensitive or irrelevant questions, which aligns with the business rules.

Preventing Responses to Political Questions:The Safety Guardrail is programmed to detect specific types of inquiries (like political questions) and prevent the model from generating responses outside its intended domain. When such queries are detected, the guardrail intervenes and provides a pre-defined response: “Sorry, I cannot answer that. I am a chatbot that can only answer questions around insurance.”

How It Works in Practice:The LLM system can include aclassification layeror trigger rules based on specific keywords related to politics. When such terms are detected, the Safety Guardrail blocks the normal generation flow and responds with the fixed message.

Why Other Options Are Less Suitable:

B (Security Guardrail): This is more focused on protecting the system from security vulnerabilities or data breaches, not controlling the conversational focus.

C (Contextual Guardrail): While context guardrails can limit responses based on context, safety guardrails are specifically about ensuring the chatbot stays within a safe conversational scope.

D (Compliance Guardrail): Compliance guardrails are often related to legal and regulatory adherence, which is not directly relevant here.

Therefore, aSafety Guardrailis the right framework to ensure the chatbot only answers insurance-related queries and avoids political discussions.

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.