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

To optimize a chunking strategy for a Retrieval-Augmented Generation (RAG) application, the Generative AI Engineer needs a structured approach to evaluating the chunking strategy, ensuring that the chosen configuration retrieves the most relevant information and leads to accurate and coherent LLM responses. Here's whyCandEare the correct strategies:

Strategy C: Evaluation Metrics (Recall, NDCG)

Define an evaluation metric: Common evaluation metrics such as recall, precision, or NDCG (Normalized Discounted Cumulative Gain) measure how well the retrieved chunks match the user's query and the expected response.

Recallmeasures the proportion of relevant information retrieved.

NDCGis often used when you want to account for both the relevance of retrieved chunks and the ranking or order in which they are retrieved.

Experiment with chunking strategies: Adjusting chunking strategies based on text structure (e.g., splitting by paragraph, chapter, or a fixed number of tokens) allows the engineer to experiment with various ways of slicing the text. Some chunks may better align with the user's query than others.

Evaluate performance: By using recall or NDCG, the engineer can methodically test various chunking strategies to identify which one yields the highest performance. This ensures that the chunking method provides the most relevant information when embedding and retrieving data from the vector store.

Strategy E: LLM-as-a-Judge Metric

Use the LLM as an evaluator: After retrieving chunks, the LLM can be used to evaluate the quality of answers based on the chunks provided. This could be framed as a "judge" function, where the LLM compares how well a given chunk answers previous user queries.

Optimize based on the LLM's judgment: By having the LLM assess previous answers and rate their relevance and accuracy, the engineer can collect feedback on how well different chunking configurations perform in real-world scenarios.

This metric could be a qualitative judgment on how closely the retrieved information matches the user's intent.

Tune chunking parameters: Based on the LLM's judgment, the engineer can adjust the chunk size or structure to better align with the LLM's responses, optimizing retrieval for future queries.

By combining these two approaches, the engineer ensures that the chunking strategy is systematically evaluated using both quantitative (recall/NDCG) and qualitative (LLM judgment) methods. This balanced optimization process results in improved retrieval relevance and, consequently, better response generation by the LLM.

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.

In the context of designing a chatbot to engage users on a gaming platform,diversity of responses(option B) is a key metric to increase user engagement and retention. Here’s why:

Diverse and Engaging Interactions:A chatbot that provides varied and interesting responses will keep users engaged, especially in an interactive environment like a gaming platform. Gamers typically enjoy dynamic and evolving conversations, anddiversity of responseshelps prevent monotony, encouraging users to interact more frequently with the bot.

Increasing Retention:By offering different types of responses to similar queries, the chatbot can create a sense of novelty and excitement, which enhances the user’s experience and makes them more likely to return to the platform.

Why Other Options Are Less Effective:

A (Randomness): Random responses can be confusing or irrelevant, leading to frustration and reducing engagement.

C (Lack of Relevance): If responses are not relevant to the user’s queries, this will degrade the user experience and lead to disengagement.

D (Repetition of Responses): Repetitive responses can quickly bore users, making the chatbot feel uninteresting and reducing the likelihood of continued interaction.

Thus,diversity of responses(option B) is the most effective way to keep users engaged and retain them on the platform.