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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Generative AI Engineer Associate |
| Exam Number: | Databricks-Generative-AI-Engineer-Associate |
| Related Certifications: | Databricks Certified Data Engineer Associate Databricks Certified Machine Learning Associate |
| Real Exam Qty: | 45-60 |
| Exam Price: | USD 200 |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 90 minutes |
| Available Languages: | English |
| Exam Format: | Multiple choice, Multiple select |
| Recommended Training: | Mosaic AI Documentation Databricks Academy - Generative AI Courses |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions |
| Exam Way: | Online proctored exam |
| Pre Condition: | No strict prerequisites, but experience with Python, machine learning fundamentals, and Databricks platform is recommended. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification |
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NEW QUESTION # 26
A Generative AI Engineer is deploying a conversational agent that provides financial-analysis insights to internal analysts at a company. The company has standardized on Microsoft 365 for all internal communications and collaboration. The analysts are distributed across multiple time zones and primarily work within Microsoft Teams channels, where they already discuss financial data and share reports. The agent needs to integrate with the company's existing Azure Active Directory for authentication and must respect the company's data-governance policies. Users should be able to interact with the agent within their existing workflow without switching applications.
Which interface should the engineer implement for this agent deployment?
Answer: C
Explanation:
Microsoft Teams integration directly satisfies the requirement to use the agent within the analysts' existing collaboration environment. Users can ask questions where they already discuss financial information, avoiding a separate portal or browser application. Databricks documents Teams integration that allows users to ask data questions through direct messages, channels, or group chats. Authentication and downstream authorization must still be configured correctly; selecting Teams does not automatically grant access to governed data. Slack conflicts with the company's standardized communication platform. A custom frontend or Databricks App can provide an effective interface, but both introduce an additional destination when the requirement explicitly prioritizes staying in Teams. The deployment should connect the Teams interface to the appropriate governed agent backend. Databricks documentation
NEW QUESTION # 27
A Generative AI Engineer is designing a RAG application for answering user questions on technical regulations as they learn a new sport.
What are the steps needed to build this RAG application and deploy it?
Answer: A
Explanation:
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.
NEW QUESTION # 28
A Generative AI Engineer is building a RAG application that will rely on context retrieved from source documents that are currently in PDF format. These PDFs can contain both text and images. They want to develop a solution using the least amount of lines of code.
Which Python package should be used to extract the text from the source documents?
Answer: B
Explanation:
* 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.
NEW QUESTION # 29
What is an effective method to preprocess prompts using custom code before sending them to an LLM?
Answer: D
Explanation:
The most effective way to preprocess prompts using custom code is to write a custom model, such as an MLflow 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. A PyFunc model 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.
NEW QUESTION # 30
A Generative AI Engineer is designing an LLM-powered live sports commentary platform. The platform provides real-time updates and LLM-generated analyses for any users who would like to have live summaries, rather than reading a series of potentially outdated news articles.
Which tool below will give the platform access to real-time data for generating game analyses based on the latest game scores?
Answer: A
Explanation:
* Problem Context: The engineer is developing an LLM-powered live sports commentary platform that needs to provide real-time updates and analyses based on the latest game scores. The critical requirement here is the capability to access and integrate real-time data efficiently with the platform for immediate analysis and reporting.
* Explanation of Options:
* Option A: DatabricksIQ: While DatabricksIQ offers integration and data processing capabilities, it is more aligned with data analytics rather than real-time feature serving, which is crucial for immediate updates necessary in a live sports commentary context.
* Option B: Foundation Model APIs: These APIs facilitate interactions with pre-trained models and could be part of the solution, but on their own, they do not provide mechanisms to access real- time game scores.
* Option C: Feature Serving: This is the correct answer as feature serving specifically refers to the real-time provision of data (features) to models for prediction. This would be essential for an LLM that generates analyses based on live game data, ensuring that the commentary is current and based on the latest events in the sport.
* Option D: AutoML: This tool automates the process of applying machine learning models to real-world problems, but it does not directly provide real-time data access, which is a critical requirement for the platform.
Thus,Option C(Feature Serving) is the most suitable tool for the platform as it directly supports the real-time data needs of an LLM-powered sports commentary system, ensuring that the analyses and updates are based on the latest available information.
NEW QUESTION # 31
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