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

The most effective way to preprocess prompts using custom code is to write a custom model, such as anMLflow PyFunc model. Here’s a breakdown of why this is the correct approach:

MLflow PyFunc Models:MLflow is a widely used platform for managing the machine learning lifecycle, including experimentation, reproducibility, and deployment. APyFuncmodel is a generic Python function model that can implement custom logic, which includes preprocessing prompts.

Preprocessing Prompts:Preprocessing could include various tasks like cleaning up the user input, formatting it according to specific rules, or augmenting it with additional context before passing it to the LLM. Writing this preprocessing as part of a PyFunc model allows the custom code to be managed, tested, and deployed easily.

Modular and Reusable:By separating the preprocessing logic into a PyFunc model, the system becomes modular, making it easier to maintain and update without needing to modify the core LLM or retrain it.

Why Other Options Are Less Suitable:

A (Modify LLM’s Internal Architecture): Directly modifying the LLM's architecture is highly impractical and can disrupt the model’s performance. LLMs are typically treated as black-box models for tasks like prompt processing.

B (Avoid Custom Code): While it’s true that LLMs haven't been explicitly trained with preprocessed prompts, preprocessing can still improve clarity and alignment with desired input formats without confusing the model.

C (Postprocessing Outputs): While postprocessing the output can be useful, it doesn't address the need for clean and well-formatted inputs, which directly affect the quality of the model's responses.

Thus, using an MLflow PyFunc model allows for flexible and controlled preprocessing of prompts in a scalable way, making it the most effective method.

 Problem Context: The Generative AI Engineer needs a model for a Retrieval-Augmented Generation (RAG) application that provides high-quality answers, where latency and throughput are not major concerns. The key factors areconfidentialityandsensitivityof the data, as well as the requirement for all processing to be confined to internal resources without external data transmission.

 Explanation of Options:

Option A: Dolly 1.5B: This model does not typically support RAG applications as it's more focused on image generation tasks.

Option B: OpenAI GPT-4: While GPT-4 is powerful for generating responses, its standard deployment involves cloud-based processing, which could violate the confidentiality requirements due to external data transmission.

Option C: BGE-large: The BGE (Big Green Engine) large model is a suitable choice if it is configured to operate on-premises or within a secure internal environment that meets regulatory requirements. Assuming this setup, BGE-large can provide high-quality answers while ensuring that data is not transmitted to third parties, thus aligning with the project's sensitivity and confidentiality needs.

Option D: Llama2-70B: Similar to GPT-4, unless specifically set up for on-premises use, it generally relies on cloud-based services, which might risk confidential data exposure.

Given the sensitivity and confidentiality concerns,BGE-largeis assumed to be configurable for secure internal use, making it the optimal choice for this scenario.

To build an LLM-powered system that accesses up-to-date news articles and stock prices, the best approach is tocreate an agentthat has access to specific tools (option D).

Agent with SQL and Web Search Capabilities:By using an agent-based architecture, the LLM can interact with external tools. The agent can query Delta tables (for up-to-date stock prices) via SQL and perform web searches to retrieve the latest news articles. This modular approach ensures the system can access both structured (stock prices) and unstructured (news) data sources dynamically.

Why This Approach Works:

SQL Queries for Stock Prices: Delta tables store stock prices, which the agent can query directly for the latest data.

Web Search for News: For news articles, the agent can generate search queries and retrieve the most relevant and recent articles, then pass them to the LLM for processing.

Why Other Options Are Less Suitable:

A (Summarizing News for Stock Prices): This convoluted approach would not ensure accuracy when retrieving stock prices, which are already structured and stored in Delta tables.

B (Stock Price Volatility Queries): While this could retrieve relevant information, it doesn't address how to obtain the most up-to-date news articles.

C (Vector Store): Storing news articles and stock prices in a vector store might not capture the real-time nature of stock data and news updates, as it relies on pre-existing data rather than dynamic querying.

Thus, using an agent with access to both SQL for querying stock prices and web search for retrieving news articles is the best approach for ensuring up-to-date and accurate responses.

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.