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| Section | Objectives |
|---|---|
| Topic 1: Responsible AI and Governance | - Model governance
|
| Topic 2: Databricks Mosaic AI Platform | - Model development and serving
|
| Topic 3: Foundations of Generative AI | - Generative AI Concepts
|
| Topic 4: Retrieval-Augmented Generation (RAG) | - RAG architecture
|
| Topic 5: Prompt Engineering | - Prompt design techniques
|
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77. Frage
A Generative Al Engineer is working with a retail company that wants to enhance its customer experience by automatically handling common customer inquiries. They are working on an LLM-powered Al solution that should improve response times while maintaining a personalized interaction. They want to define the appropriate input and LLM task to do this.
Which input/output pair will do this?
Antwort: B
Begründung:
The task described in the question involves enhancing customer experience by automatically handling common customer inquiries using an LLM-powered AI solution. This requires the system to process input data (customer inquiries) and generate personalized, relevant responses efficiently. Let's evaluate the options step-by-step in the context of Databricks Generative AI Engineer principles, which emphasize leveraging LLMs for tasks like question answering, summarization, and retrieval-augmented generation (RAG).
Option A: Input: Customer reviews; Output: Group the reviews by users and aggregate per-user average rating, then respond This option focuses on analyzing customer reviews to compute average ratings per user. While this might be useful for sentiment analysis or user profiling, it does not directly address the goal of handling common customer inquiries or improving response times for personalized interactions. Customer reviews are typically feedback data, not real-time inquiries requiring immediate responses.
Databricks Reference: Databricks documentation on LLMs (e.g., "Building LLM Applications with Databricks") emphasizes that LLMs excel at tasks like question answering and conversational responses, not just aggregation or statistical analysis of reviews.
Option B: Input: Customer service chat logs; Output: Group the chat logs by users, followed by summarizing each user's interactions, then respond This option uses chat logs as input, which aligns with customer service scenarios. However, the output-grouping by users and summarizing interactions-focuses on user-specific summaries rather than directly addressing inquiries. While summarization is an LLM capability, this approach lacks the specificity of finding answers to common questions, which is central to the problem.
Databricks Reference: Per Databricks' "Generative AI Cookbook," LLMs can summarize text, but for customer service, the emphasis is on retrieval and response generation (e.g., RAG workflows) rather than user interaction summaries alone.
Option C: Input: Customer service chat logs; Output: Find the answers to similar questions and respond with a summary This option uses chat logs (real customer inquiries) as input and tasks the LLM with identifying answers to similar questions, then providing a summarized response. This directly aligns with the goal of handling common inquiries efficiently while maintaining personalization (by referencing past interactions or similar cases). It leverages LLM capabilities like semantic search, retrieval, and response generation, which are core to Databricks' LLM workflows.
Databricks Reference: From Databricks documentation ("Building LLM-Powered Applications," 2023), an exact extract states: "For customer support use cases, LLMs can be used to retrieve relevant answers from historical data like chat logs and generate concise, contextually appropriate responses." This matches Option C's approach of finding answers and summarizing them.
Option D: Input: Customer reviews; Output: Classify review sentiment
This option focuses on sentiment classification of reviews, which is a valid LLM task but unrelated to handling customer inquiries or improving response times in a conversational context. It's more suited for feedback analysis than real-time customer service.
Databricks Reference: Databricks' "Generative AI Engineer Guide" notes that sentiment analysis is a common LLM task, but it's not highlighted for real-time conversational applications like customer support.
Conclusion: Option C is the best fit because it uses relevant input (chat logs) and defines an LLM task (finding answers and summarizing) that meets the requirements of improving response times and maintaining personalized interaction. This aligns with Databricks' recommended practices for LLM-powered customer service solutions, such as retrieval-augmented generation (RAG) workflows.
78. Frage
A team uses Mosaic AI Vector Search to retrieve documents for their Retrieval-Augmented Generation (RAG) pipeline. The search query returns five relevant documents, and the first three are added to the prompt as context. Performance evaluation with Agent Evaluation shows that some lower-ranked retrieved documents have higher context relevancy scores than higher-ranked documents. Which option should the team consider to optimize this workflow?
Antwort: B
Begründung:
The scenario describes a common "retrieval gap" where the initial bi-encoder (embedding model) used for vector search identifies relevant documents but does not rank them perfectly. This happens because embedding models represent entire documents as a single vector, which can lose nuance. The standard engineering solution is to implement a Reranker (Cross-Encoder). Unlike embedding models, a reranker processes the query and a candidate document simultaneously, allowing it to capture deep semantic interactions between the two. In a Mosaic AI workflow, after the vector search retrieves the top $k$ documents, the reranker evaluates those specific $k$ documents to produce a more accurate relevance score. This ensures that the most contextually relevant documents are placed at the top of the list (and thus the top of the LLM prompt), which is crucial because LLMs are sensitive to document order and often prioritize information found at the beginning of the context.
79. Frage
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?
Antwort: A
Begründung:
The Generative AI Engineer needs to follow a methodical pipeline to build and deploy a Retrieval- Augmented Generation (RAG) application. The steps outlined in optionBaccurately 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.
80. Frage
A Generative Al Engineer at an automotive company would like to build a question-answering chatbot for customers to inquire about their vehicles. They have a database containing various documents of different vehicle makes, their hardware parts, and common maintenance information.
Which of the following components will NOT be useful in building such a chatbot?
Antwort: B
Begründung:
The task involves building a question-answering chatbot for an automotive company using a database of vehicle-related documents. The chatbot must efficiently process customer inquiries and provide accurate responses. Let's evaluate each component to determine which isnotuseful, per Databricks Generative AI Engineer principles.
* Option A: Response-generating LLM
* An LLM is essential for generating natural language responses to customer queries based on retrieved information. This is a core component of any chatbot.
* Databricks Reference:"The response-generating LLM processes retrieved context to produce coherent answers"("Building LLM Applications with Databricks," 2023).
* Option B: Invite users to submit long, rather than concise, questions
* Encouraging long questions is a user interaction design choice, not a technical component of the chatbot's architecture. Moreover, long, verbose questions can complicate intent detection and retrieval, reducing efficiency and accuracy-counter to best practices for chatbot design. Concise questions are typically preferred for clarity and performance.
* Databricks Reference: While not explicitly stated, Databricks' "Generative AI Cookbook" emphasizes efficient query processing, implying that simpler, focused inputs improve LLM performance. Inviting long questions doesn't align with this.
* Option C: Vector database
* A vector database stores embeddings of the vehicle documents, enabling fast retrieval of relevant information via semantic search. This is critical for a question-answering system with a large document corpus.
* Databricks Reference:"Vector databases enable scalable retrieval of context from large datasets"("Databricks Generative AI Engineer Guide").
* Option D: Embedding model
* An embedding model converts text (documents and queries) into vector representations for similarity search. It's a foundational component for retrieval-augmented generation (RAG) in chatbots.
* Databricks Reference:"Embedding models transform text into vectors, facilitating efficient matching of queries to documents"("Building LLM-Powered Applications").
Conclusion: Option B is not a usefulcomponentin building the chatbot. It's a user-facing suggestion rather than a technical building block, and it could even degrade performance by introducing unnecessary complexity. Options A, C, and D are all integral to a Databricks-aligned chatbot architecture.
81. Frage
A Generative AI Engineer is creating an LLM-powered application that will need access to up-to-date news articles and stock prices.
The design requires the use of stock prices which are stored in Delta tables and finding the latest relevant news articles by searching the internet.
How should the Generative AI Engineer architect their LLM system?
Antwort: D
Begründung:
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.
82. Frage
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