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| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Generative AI Engineer Associate |
| Exam Number: | Generative AI Engineer Associate |
| Related Certifications: | Databricks Certified Data Engineer Associate Databricks Certified Machine Learning Associate |
| Real Exam Qty: | 45 |
| Exam Duration: | 90 minutes |
| Exam Price: | $200 USD |
| Certificate Validity Period: | 2 years |
| Passing Score: | Scaled Score (approx. 70%) |
| Available Languages: | Japanese, Korean, English, Portuguese (Brazil) |
| Exam Format: | Multiple Choice, Multiple Select |
| Sample Questions: | Databricks Databricks-Generative-AI-Engineer-Associate Sample Questions |
| Exam Way: | Online (Proctored) or Test Center |
| Pre Condition: | None (Recommended: 6+ months hands-on experience) |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/genai-engineer-associate |
>> Databricks-Generative-AI-Engineer-Associate證照指南 <<
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問題 #27
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?
答案：C
解題說明：
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.
問題 #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?
答案：D
解題說明：
* 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.
問題 #29
Generative AI Engineer at an electronics company just deployed a RAG application for customers to ask questions about products that the company carries. However, they received feedback that the RAG response often returns information about an irrelevant product.
What can the engineer do to improve the relevance of the RAG's response?
答案：D
解題說明：
In a Retrieval-Augmented Generation (RAG) system, the key to providing relevant responses lies in the quality of the retrieved context. Here's why option A is the most appropriate solution:
* Context Relevance:The RAG model generates answers based on retrieved documents or context. If the retrieved information is about an irrelevant product, it suggests that the retrieval step is failing to select the right context. The Generative AI Engineer must first assess the quality of what is being retrieved and ensure it is pertinent to the query.
* Vector Search and Embedding Similarity:RAG typically uses vector search for retrieval, where embeddings of the query are matched against embeddings of product descriptions. Assessing the semantic similarity searchprocess ensures that the closest matches are actually relevant to the query.
* Fine-tuning the Retrieval Process:By improving theretrieval quality, such as tuning the embeddings or adjusting the retrieval strategy, the system can return more accurate and relevant product information.
* Why Other Options Are Less Suitable:
* B (Caching FAQs): Caching can speed up responses for frequently asked questions but won't improve the relevance of the retrieved content for less frequent or new queries.
* C (Use a Different LLM): Changing the LLM only affects the generation step, not the retrieval process, which is the core issue here.
* D (Different Semantic Search Algorithm): This could help, but the first step is to evaluate the current retrieval context before replacing the search algorithm.
Therefore, improving and assessing the quality of the retrieved context (option A) is the first step to fixing the issue of irrelevant product information.
問題 #30
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?
答案：C
解題說明：
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.
問題 #31
Generative AI Engineer at an electronics company just deployed a RAG application for customers to ask questions about products that the company carries. However, they received feedback that the RAG response often returns information about an irrelevant product.
What can the engineer do to improve the relevance of the RAG's response?
答案：D
解題說明：
In a Retrieval-Augmented Generation (RAG) system, the key to providing relevant responses lies in the quality of the retrieved context. Here's why option A is the most appropriate solution:
Context Relevance:
The RAG model generates answers based on retrieved documents or context. If the retrieved information is about an irrelevant product, it suggests that the retrieval step is failing to select the right context. The Generative AI Engineer must first assess the quality of what is being retrieved and ensure it is pertinent to the query.
Vector Search and Embedding Similarity:
RAG typically uses vector search for retrieval, where embeddings of the query are matched against embeddings of product descriptions. Assessing the semantic similarity search process ensures that the closest matches are actually relevant to the query.
Fine-tuning the Retrieval Process:
By improving the retrieval quality, such as tuning the embeddings or adjusting the retrieval strategy, the system can return more accurate and relevant product information.
Why Other Options Are Less Suitable:
B (Caching FAQs): Caching can speed up responses for frequently asked questions but won't improve the relevance of the retrieved content for less frequent or new queries.
C (Use a Different LLM): Changing the LLM only affects the generation step, not the retrieval process, which is the core issue here.
D (Different Semantic Search Algorithm): This could help, but the first step is to evaluate the current retrieval context before replacing the search algorithm.
Therefore, improving and assessing the quality of the retrieved context (option A) is the first step to fixing the issue of irrelevant product information.
問題 #32
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