Databricks-Generative-AI-Engineer-Associate Databricks Certified Generative AI Engineer Associate Exam Practice Test

Answer : B

The Generative AI Engineer needs to follow a methodical pipeline to build and deploy a Retrieval-Augmented Generation (RAG) application. The steps outlined in option B accurately reflect this process:

Ingest documents from a source: This is the first step, where the engineer collects documents (e.g., technical regulations) that will be used for retrieval when the application answers user questions.

Index the documents and save to Vector Search: Once the documents are ingested, they need to be embedded using a technique like embeddings (e.g., with a pre-trained model like BERT) and stored in a vector database (such as Pinecone or FAISS). This enables fast retrieval based on user queries.

User submits queries against an LLM: Users interact with the application by submitting their queries. These queries will be passed to the LLM.

LLM retrieves relevant documents: The LLM works with the vector store to retrieve the most relevant documents based on their vector representations.

LLM generates a response: Using the retrieved documents, the LLM generates a response that is tailored to the user's question.

Evaluate model: After generating responses, the system must be evaluated to ensure the retrieved documents are relevant and the generated response is accurate. Metrics such as accuracy, relevance, and user satisfaction can be used for evaluation.

Deploy it using Model Serving: Once the RAG pipeline is ready and evaluated, it is deployed using a model-serving platform such as Databricks Model Serving. This enables real-time inference and response generation for users.

By following these steps, the Generative AI Engineer ensures that the RAG application is both efficient and effective for the task of answering technical regulation questions.

Answer : C

Problem Context: The engineer needs to extract text from PDF documents, which may contain both text and images. The goal is to find a Python package that simplifies this task using the least amount of code.

Explanation of Options:

Option A: flask: Flask is a web framework for Python, not suitable for processing or extracting content from PDFs.

Option B: beautifulsoup: Beautiful Soup is designed for parsing HTML and XML documents, not PDFs.

Option C: unstructured: This Python package is specifically designed to work with unstructured data, including extracting text from PDFs. It provides functionalities to handle various types of content in documents with minimal coding, making it ideal for the task.

Option D: numpy: Numpy is a powerful library for numerical computing in Python and does not provide any tools for text extraction from PDFs.

Given the requirement, Option C (unstructured) is the most appropriate as it directly addresses the need to efficiently extract text from PDF documents with minimal code.

Answer : B

Problem Context: The goal is to deploy a trained LLM on Databricks in the simplest and most integrated manner.

Explanation of Options:

Option A: This method involves unnecessary steps like logging the model as a pickle object, which is not the most efficient path in a Databricks environment.

Option B: Logging the model with MLflow during training and then using MLflow's API to register and start serving the model is straightforward and leverages Databricks' built-in functionalities for seamless model deployment.

Option C: Building and running a Docker container is a complex and less integrated approach within the Databricks ecosystem.

Option D: Using Flask and Gunicorn is a more manual approach and less integrated compared to the native capabilities of Databricks and MLflow.

Option B provides the most straightforward and efficient process, utilizing Databricks' ecosystem to its full advantage for deploying models.

Answer : D

To mitigate the issue of the LLM including explanations of how summaries are generated in its output, the best approach is to adjust the training or prompt structure. Here's why Option D is effective:

Few-shot Learning: By providing specific examples of how the desired output should look (i.e., just the summary without explanation), the model learns the preferred format. This few-shot learning approach helps the model understand not only what content to generate but also how to format its responses.

Prompt Engineering: Adjusting the user prompt to specify the desired output format clearly can guide the LLM to produce summaries without additional explanatory text. Effective prompt design is crucial in controlling the behavior of generative models.

Why Other Options Are Less Suitable:

A: While technically feasible, splitting the output by newline and truncating could lead to loss of important content or create awkward breaks in the summary.

B: Tuning chunk sizes or changing embedding models does not directly address the issue of the model's tendency to generate explanations along with summaries.

C: Revisiting document ingestion logic ensures accurate source data but does not influence how the model formats its output.

By using few-shot examples and refining the prompt, the engineer directly influences the output format, making this approach the most targeted and effective solution.

Answer : D

Problem Context: The problem involves matching team members to new projects based on two main factors:

Availability: Ensure the team members are available during the project dates.

Profile-Project Match: Use the employee profiles (unstructured text) to find the best match for a project's scope (also unstructured text).

The two main inputs are the employee profiles and project scopes, both of which are unstructured. This means traditional rule-based systems (e.g., simple keyword matching) would be inefficient, especially when working with large datasets.

Explanation of Options: Let's break down the provided options to understand why D is the most optimal answer.

Option A suggests embedding project scopes into a vector store and then performing retrieval using team member profiles. While embedding project scopes into a vector store is a valid technique, it skips an important detail: the focus should primarily be on embedding employee profiles because we're matching the profiles to a new project, not the other way around.

Option B involves using a large language model (LLM) to extract keywords from the project scope and perform keyword matching on employee profiles. While LLMs can help with keyword extraction, this approach is too simplistic and doesn't leverage advanced retrieval techniques like vector embeddings, which can handle the nuanced and rich semantics of unstructured data. This approach may miss out on subtle but important similarities.

Option C suggests calculating a similarity score between each team member's profile and project scope. While this is a good idea, it doesn't specify how to handle the unstructured nature of data efficiently. Iterating through each member's profile individually could be computationally expensive in large teams. It also lacks the mention of using a vector store or an efficient retrieval mechanism.

Option D is the correct approach. Here's why:

Embedding team profiles into a vector store: Using a vector store allows for efficient similarity searches on unstructured data. Embedding the team member profiles into vectors captures their semantics in a way that is far more flexible than keyword-based matching.

Using project scope for retrieval: Instead of matching keywords, this approach suggests using vector embeddings and similarity search algorithms (e.g., cosine similarity) to find the team members whose profiles most closely align with the project scope.

Filtering based on availability: Once the best-matched candidates are retrieved based on profile similarity, filtering them by availability ensures that the system provides a practically useful result.

This method efficiently handles large-scale datasets by leveraging vector embeddings and similarity search techniques, both of which are fundamental tools in Generative AI engineering for handling unstructured text.

Technical References:

Vector embeddings: In this approach, the unstructured text (employee profiles and project scopes) is converted into high-dimensional vectors using pretrained models (e.g., BERT, Sentence-BERT, or custom embeddings). These embeddings capture the semantic meaning of the text, making it easier to perform similarity-based retrieval.

Vector stores: Solutions like FAISS or Milvus allow storing and retrieving large numbers of vector embeddings quickly. This is critical when working with large teams where querying through individual profiles sequentially would be inefficient.

LLM Integration: Large language models can assist in generating embeddings for both employee profiles and project scopes. They can also assist in fine-tuning similarity measures, ensuring that the retrieval system captures the nuances of the text data.

Filtering: After retrieving the most similar profiles based on the project scope, filtering based on availability ensures that only team members who are free for the project are considered.

This system is scalable, efficient, and makes use of the latest techniques in Generative AI, such as vector embeddings and semantic search.

Answer : C

In a Retrieval-Augmented Generation (RAG) application built to answer questions about internal documents, especially when the dataset contains irrelevant content, it's crucial to guide the system to focus on the right information. The best way to achieve this is by including a clear instruction in the system prompt (option C).

System Prompt as Guidance: The system prompt is an effective way to instruct the LLM to limit its focus to SnoPen AI-related content. By clearly specifying that the model should avoid answering questions unrelated to SnoPen AI, you add an additional layer of control that helps the model stay on-topic, even if irrelevant content is present in the dataset.

Why This Approach Works: The prompt acts as a guiding principle for the model, narrowing its focus to specific domains. This prevents the model from generating answers based on irrelevant content, such as advertisements or news unrelated to SnoPen AI.

Why Other Options Are Less Suitable:

A (Keep All Articles): Retaining all content, including irrelevant materials, without any filtering makes the system prone to generating answers based on unwanted data.

B (Include in the System Prompt about SnoPen AI): This option doesn't address irrelevant content directly, and without filtering, the model might still retrieve and use irrelevant data.

D (Consolidating Documents into a Single Chunk): Grouping documents into a single chunk makes the retrieval process less efficient and won't help filter out irrelevant content effectively.

Therefore, instructing the system in the prompt not to answer questions unrelated to SnoPen AI (option C) is the best approach to ensure the system filters out irrelevant information.

Answer : C, D

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.

Options C and D are 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.