Databricks-Generative-AI-Engineer-Associate復習過去問 & Databricks-Generative-AI-Engineer-Associate勉強ガイド

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Databricks Databricks-Generative-AI-Engineer-Associate 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Evaluation and Monitoring: This topic is all about selecting an LLM choice and key metrics. Moreover, Generative AI Engineers learn about evaluating model performance. Lastly, the topic includes sub-topics about inference logging and usage of Databricks features.
トピック 2	Application Development: In this topic, Generative AI Engineers learn about tools needed to extract data, Langchain similar tools, and assessing responses to identify common issues. Moreover, the topic includes questions about adjusting an LLM's response, LLM guardrails, and the best LLM based on the attributes of the application.
トピック 3	Data Preparation: Generative AI Engineers covers a chunking strategy for a given document structure and model constraints. The topic also focuses on filter extraneous content in source documents. Lastly, Generative AI Engineers also learn about extracting document content from provided source data and format.
トピック 4	Governance: Generative AI Engineers who take the exam get knowledge about masking techniques, guardrail techniques, and legal licensing requirements in this topic.

>> Databricks-Generative-AI-Engineer-Associate復習過去問 <<

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Databricks Certified Generative AI Engineer Associate 認定 Databricks-Generative-AI-Engineer-Associate 試験問題 (Q84-Q89):

質問 # 84
A Generative AI Engineer is building a compound AI system for an organization. The goal is to automate the processing of incoming customer event reports against a coding system and corporate-guidelines documentation. The system must handle three distinct user-request types: answering questions from guidelines documents, extracting specific event codes from reviewers' notes, and routing ambiguous requests to the appropriate specialized handler. All three capabilities must operate under a single entry point that interprets user intent and delegates accordingly.
Which Agent Brick should serve as the top-level orchestrator in this architecture?

A. Multi-Agent Supervisor, because it can be used without Knowledge Assistant and Information Extraction agents.
B. Multi-Agent Supervisor, because it interprets incoming requests and delegates tasks to specialized sub- agents.
C. Knowledge Assistant, because the chatbot interface can handle multi-turn conversations.
D. Knowledge Assistant, because it can be configured with multiple vector indexes to handle all three request types simultaneously.

正解：B

解説：
The Multi-Agent Supervisor is designed to coordinate specialized agents and tools behind a unified entry point. It interprets the incoming request, selects suitable capabilities, delegates work, and combines results when necessary. In this scenario, document questions can be handled by a knowledge-retrieval specialist, while event-code extraction can be assigned to a specialized extraction capability. The supervisor provides the coordination needed to select between those tasks and handle requests requiring multiple steps. A Knowledge Assistant's conversational interface does not itself make it the appropriate general-purpose orchestrator.
Adding vector indexes also does not establish specialized extraction and routing behavior. Although option A names the same component, its justification misses the central requirement: coordinated delegation across distinct capabilities. Databricks documentation

質問 # 85
A Generative AI Engineer has been asked to design an LLM-based application that accomplishes the following business objective: answer employee HR questions using HR PDF documentation.
Which set of high level tasks should the Generative AI Engineer's system perform?

A. Split HR documentation into chunks and embed into a vector store. Use the employee question to retrieve best matched chunks of documentation, and use the LLM to generate a response to the employee based upon the documentation retrieved.
B. Use an LLM to summarize HR documentation. Provide summaries of documentation and user query into an LLM with a large context window to generate a response to the user.
C. Create an interaction matrix of historical employee questions and HR documentation. Use ALS to factorize the matrix and create embeddings. Calculate the embeddings of new queries and use them to find the best HR documentation. Use an LLM to generate a response to the employee question based upon the documentation retrieved.
D. Calculate averaged embeddings for each HR document, compare embeddings to user query to find the best document. Pass the best document with the user query into an LLM with a large context window to generate a response to the employee.

正解：A

解説：
To design an LLM-based application that can answer employee HR questions using HR PDF documentation, the most effective approach is option D. Here's why:
* Chunking and Vector Store Embedding:HR documentation tends to be lengthy, so splitting it into smaller, manageable chunks helps optimize retrieval. These chunks are then embedded into avector store(a database that stores vector representations of text). Each chunk of text is transformed into an embeddingusing a transformer-based model, which allows for efficient similarity-based retrieval.
* Using Vector Search for Retrieval:When an employee asks a question, the system converts their query into an embedding as well. This embedding is then compared with the embeddings of the document chunks in the vector store. The most semantically similar chunks are retrieved, which ensures that the answer is based on the most relevant parts of the documentation.
* LLM to Generate a Response:Once the relevant chunks are retrieved, these chunks are passed into the LLM, which uses them as context to generate a coherent and accurate response to the employee's question.
* Why Other Options Are Less Suitable:
* A (Calculate Averaged Embeddings): Averaging embeddings might dilute important information. It doesn't provide enough granularity to focus on specific sections of documents.
* B (Summarize HR Documentation): Summarization loses the detail necessary for HR-related queries, which are often specific. It would likely miss the mark for more detailed inquiries.
* C (Interaction Matrix and ALS): This approach is better suited for recommendation systems and not for HR queries, as it's focused on collaborative filtering rather than text-based retrieval.
Thus, option D is the most effective solution for providing precise and contextual answers based on HR documentation.

質問 # 86
A team wants to serve a code generation model as an assistant for their software developers. It should support multiple programming languages. Quality is the primary objective.
Which of the Databricks Foundation Model APIs, or models available in the Marketplace, would be the best fit?

A. Llama2-70b
B. MPT-7b
C. BGE-large
D. CodeLlama-34B

正解：D

解説：
For a code generation model that supports multiple programming languages and where quality is the primary objective,CodeLlama-34Bis the most suitable choice. Here's the reasoning:
* Specialization in Code Generation:CodeLlama-34B is specifically designed for code generation tasks.
This model has been trained with a focus on understanding and generating code, which makes it particularly adept at handling various programming languages and coding contexts.
* Capacity and Performance:The "34B" indicates a model size of 34 billion parameters, suggesting a high capacity for handling complex tasks and generating high-quality outputs. The large model size typically correlates with better understanding and generation capabilities in diverse scenarios.
* Suitability for Development Teams:Given that the model is optimized for code, it will be able to assist software developers more effectively than general-purpose models. It understands coding syntax, semantics, and the nuances of different programming languages.
* Why Other Options Are Less Suitable:
* A (Llama2-70b): While also a large model, it's more general-purpose and may not be as fine- tuned for code generation as CodeLlama.
* B (BGE-large): This model may not specifically focus on code generation.
* C (MPT-7b): Smaller than CodeLlama-34B and likely less capable in handling complex code generation tasks at high quality.
Therefore, for a high-quality, multi-language code generation application,CodeLlama-34B(option D) is the best fit.

質問 # 87
A Generative AI Engineer is developing an LLM application that users can use to generate personalized birthday poems based on their names.
Which technique would be most effective in safeguarding the application, given the potential for malicious user inputs?

A. Implement a safety filter that detects any harmful inputs and ask the LLM to respond that it is unable to assist
B. Reduce the time that the users can interact with the LLM
C. Ask the LLM to remind the user that the input is malicious but continue the conversation with the user
D. Increase the amount of compute that powers the LLM to process input faster

正解：A

解説：
In this case, the Generative AI Engineer is developing an application to generate personalized birthday poems, but there's a need to safeguard againstmalicious user inputs. The best solution is to implement asafety filter (option A) to detect harmful or inappropriate inputs.
* Safety Filter Implementation:Safety filters are essential for screening user input and preventing inappropriate content from being processed by the LLM. These filters can scan inputs for harmful language, offensive terms, or malicious content and intervene before the prompt is passed to the LLM.
* Graceful Handling of Harmful Inputs:Once the safety filter detects harmful content, the system can provide a message to the user, such as "I'm unable to assist with this request," instead of processing or responding to malicious input. This protects the system from generating harmful content and ensures a controlled interaction environment.
* Why Other Options Are Less Suitable:
* B (Reduce Interaction Time): Reducing the interaction time won't prevent malicious inputs from being entered.
* C (Continue the Conversation): While it's possible to acknowledge malicious input, it is not safe to continue the conversation with harmful content. This could lead to legal or reputational risks.
* D (Increase Compute Power): Adding more compute doesn't address the issue of harmful content and would only speed up processing without resolving safety concerns.
Therefore, implementing asafety filterthat blocks harmful inputs is the most effective technique for safeguarding the application.

質問 # 88
A Generative AI Engineer has deployed a customer-support agent in production that retrieves product documentation and generates responses. SMEs have been reviewing agent responses and providing feedback through a web interface that captures ratings of 1-5 stars and written comments. The engineer needs to systematically collect this feedback and use it to create an evaluation dataset that can be used to compare future agent versions against the current baseline performance.
Which approach should the engineer use to accomplish this task?

A. Log the SME ratings and comments directly to a Delta table with the corresponding user queries and agent responses, then use MLflow to create an evaluation dataset from this table and register it for future agent evaluations.
B. Export only the written SME comments to a text file and manually score them using a custom script, then use the script's output as the evaluation dataset for future agent comparisons.
C. Use Unity Catalog to create a view that filters only 5-star-rated interactions, then register this view as the evaluation dataset to benchmark all future agent versions.
D. Use the customer review app to collect SME feedback, then directly deploy the highest-rated responses as the new agent baseline without storing them as a formal evaluation dataset.

正解：A

解説：
Option B preserves the relationships between each user query, generated response, and expert assessment.
That context is necessary to turn production feedback into reusable evaluation cases. The engineer can curate representative examples from the Delta table, map them into MLflow's evaluation schema, and retain the dataset for consistent comparisons between agent versions. Where correctness scoring is required, experts should provide corrected answers or expected facts; a star rating alone is not a ground-truth answer. Keeping only written comments loses essential request context. Selecting only five-star interactions biases the dataset and excludes failure cases that future versions should improve. Deploying highly rated responses is not a substitute for evaluating an updated agent against a stable, representative benchmark. Databricks documentation

質問 # 89
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