Databricks-Generative-AI-Engineer-Associate Exam Dumps : Databricks Certified Generative AI Engineer Associate

The task involves improving a Retrieval-Augmented Generation (RAG) application’s performance by experimenting with embedding models. The choice of embedding model impacts retrieval accuracy,which is critical for RAG systems. Let’s evaluate the options based on Databricks Generative AI Engineer best practices.

Option A: Pick an embedding model trained on related domain knowledge

Embedding models trained on domain-specific data (e.g., industry-specific corpora) produce vectors that better capture the semantics of the application’s context, improving retrieval relevance. For RAG, this is a key strategy to enhance performance.

Databricks Reference:"For optimal retrieval in RAG systems, select embedding models aligned with the domain of your data"("Building LLM Applications with Databricks," 2023).

Option B: Pick the most recent and most performant open LLM released at the time

LLMs are not embedding models; they generate text, not embeddings for retrieval. While recent LLMs may be performant for generation, this doesn’t address the embedding step in RAG. This option misunderstands the component being selected.

Databricks Reference: Embedding models and LLMs are distinct in RAG workflows:"Embedding models convert text to vectors, while LLMs generate responses"("Generative AI Cookbook").

Option C: Pick the embedding model ranked highest on the Massive Text Embedding Benchmark (MTEB) leaderboard hosted by HuggingFace

The MTEB leaderboard ranks models across general tasks, but high overall performance doesn’t guarantee suitability for a specific domain. A top-ranked model might excel in generic contexts but underperform on the engineer’s unique data.

Databricks Reference: General performance is less critical than domain fit:"Benchmark rankings provide a starting point, but domain-specific evaluation is recommended"("Databricks Generative AI Engineer Guide").

Option D: Pick an embedding model with multilingual support to support potential multilingual user questions

Multilingual support is useful only if the application explicitly requires it. Without evidence of multilingual needs, this adds complexity without guaranteed performance gains for the current use case.

Databricks Reference:"Choose features like multilingual support based on application requirements"("Building LLM-Powered Applications").

Conclusion: Option A is the best strategy because it prioritizes domain relevance, directly improving retrieval accuracy in a RAG system—aligning with Databricks’ emphasis on tailoring models to specific use cases.

Problem Context: After switching to a model with a shorter context length, the error message indicating that the prompt token count has exceeded the limit suggests that the input to the model is too large.

Explanation of Options:

Option A: Use a smaller embedding model to generate– This wouldn't necessarily address the issue of prompt size exceeding the model’s token limit.

Option B: Reduce the maximum output tokens of the new model– This option affects the output length, not the size of the input being too large.

Option C: Decrease the chunk size of embedded documents– This would help reduce the size of each document chunk fed into the model, ensuring that the input remains within the model's context length limitations.

Option D: Reduce the number of records retrieved from the vector database– By retrieving fewer records, the total input size to the model can be managed more effectively, keeping it within the allowable token limits.

Option E: Retrain the response generating model using ALiBi– Retraining the model is contrary to the stipulation not to change the response generating model.

OptionsCandDare the most effective solutions to manage the model’s shorter context length without changing the model itself, by adjusting the input size both in terms of individual document size and total documents retrieved.

 Problem Context: The goal is to parse emails to extract certain pieces of information and output this in a structured JSON format. Clarity and specificity in the prompt design will ensure higher accuracy in the LLM’s responses.

 Explanation of Options:

Option A: Provides a general guideline but lacks an example, which helps an LLM understand the exact format expected.

Option B: Includes a clear instruction and a specific example of the output format. Providing an example is crucial as it helps set the pattern and format in which the information should be structured, leading to more accurate results.

Option C: Does not specify that the output should be in JSON format, thus not meeting the requirement.

Option D: While it correctly asks for JSON format, it lacks an example that would guide the LLM on how to structure the JSON correctly.

Therefore,Option Bis optimal as it not only specifies the required format but also illustrates it with an example, enhancing the likelihood of accurate extraction and formatting by the LLM.