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Databricks Databricks-Generative-AI-Engineer-Associate Exam Syllabus Topics:

Section	Objectives
Topic 1: Responsible AI and Governance	- Model governance 1. Access control and monitoring 2. Auditability and compliance - AI safety and ethics 1. Content safety filtering 2. Bias and fairness considerations
Topic 2: Foundations of Generative AI	- Large Language Models (LLMs) 1. Tokenization and embeddings 2. Transformer architecture basics 3. Pretraining vs fine-tuning concepts - Generative AI Concepts 1. Text generation principles 2. Hallucinations and mitigation strategies
Topic 3: Databricks Mosaic AI Platform	- Model development and serving 1. Endpoint deployment concepts 2. Using Databricks Model Serving - MLflow for GenAI 1. Model registry usage 2. Experiment tracking
Topic 4: Retrieval-Augmented Generation (RAG)	- Vector databases and embeddings 1. Embedding generation and similarity search 2. Vector index construction - RAG architecture 1. Document ingestion pipelines 2. Context retrieval and grounding
Topic 5: Prompt Engineering	- Prompt design techniques 1. Role prompting and instruction tuning patterns 2. Zero-shot and few-shot prompting - Prompt evaluation 1. Iterative prompt refinement 2. Output quality assessment

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多くの受験生の反応によって、It-Passportsの模擬試験は全面的で質量が高いです。Databricks試験は難しいですから、参考資料がないなら、試験に合格するのは簡単ではありません。我々の的中率が高くて安いDatabricks-Generative-AI-Engineer-Associate問題集を利用して試験に気楽に合格することができます。弊社の問題集がありましたら、易く成功できます。

Databricks Certified Generative AI Engineer Associate 認定 Databricks-Generative-AI-Engineer-Associate 試験問題 (Q35-Q40):

質問 # 35
A Generative Al Engineer is tasked with developing a RAG application that will help a small internal group of experts at their company answer specific questions, augmented by an internal knowledge base. They want the best possible quality in the answers, and neither latency nor throughput is a huge concern given that the user group is small and they're willing to wait for the best answer. The topics are sensitive in nature and the data is highly confidential and so, due to regulatory requirements, none of the information is allowed to be transmitted to third parties.
Which model meets all the Generative Al Engineer's needs in this situation?

A. BGE-large
B. Llama2-70B
C. Dolly 1.5B
D. OpenAI GPT-4

正解：A

解説：
Problem Context : The Generative AI Engineer needs a model for a Retrieval-Augmented Generation (RAG) application that provides high-quality answers, where latency and throughput are not major concerns.
The key factors are confidentiality and sensitivity of the data, as well as the requirement for all processing to be confined to internal resources without external data transmission.
Explanation of Options :
* Option A: Dolly 1.5B : This model does not typically support RAG applications as it ' s more focused on image generation tasks.
* Option B: OpenAI GPT-4 : While GPT-4 is powerful for generating responses, its standard deployment involves cloud-based processing, which could violate the confidentiality requirements due to external data transmission.
* Option C: BGE-large : The BGE (Big Green Engine) large model is a suitable choice if it is configured to operate on-premises or within a secure internal environment that meets regulatory requirements. Assuming this setup, BGE-large can provide high-quality answers while ensuring that data is not transmitted to third parties, thus aligning with the project ' s sensitivity and confidentiality needs.
* Option D: Llama2-70B : Similar to GPT-4, unless specifically set up for on-premises use, it generally relies on cloud-based services, which might risk confidential data exposure.
Given the sensitivity and confidentiality concerns, BGE-large is assumed to be configurable for secure internal use, making it the optimal choice for this scenario.

質問 # 36
A Generative AI Engineer is using LangGraph to define multiple tools in a single agentic application. They want to enable the main orchestrator LLM to decide on its own which tools are most appropriate to call for a given prompt. To do this, they must determine the general flow of the code. Which sequence will do this?

A. 1. Define or import the tools 2. Define the agent 3. Initialize the agent with ReAct, the LLM, and the tools
B. 1. Define the tools inside the agents 2. Load the agents into the LLM 3. Instruct the LLM to use COT reasoning to determine the appropriate agent
C. 1. Define or import the tools 2. Add tools and LLM to the agent 3. Create the ReAct agent
D. 1. Define the tools 2. Load each tool into a separate agent 3. Instruct the LLM to use ReAct to call the appropriate agent

正解：A

解説：
In modern agentic frameworks like LangGraph or LangChain, the standard workflow for creating an autonomous tool-calling agent follows a specific sequence. First, tools must be defined (often as Python functions with clear docstrings, which the LLM uses to understand the tool ' s purpose). Second, the agent logic is defined, which specifies how the LLM should think. Third, the agent is initialized using a logic pattern like ReAct (Reason + Act). The ReAct framework is essential here because it enables the " orchestrator " loop: the LLM receives a prompt, generates a " Thought " about which tool to use, generates an
" Action " to call that tool, receives an " Observation " (the tool ' s output), and repeats until it can provide a final answer. Loading tools into " separate agents " (C) or defining tools " inside " agents (D) are non-standard patterns that add unnecessary complexity and do not align with the centralized orchestration model required for LangGraph.

質問 # 37
A Generative AI Engineer is evaluating a customer-support agent in Databricks. The team needs to score each response on a domain-specific policy: the answer must cite an approved refund rule and must not mention unsupported escalation paths. Built-in evaluation metrics do not capture this logic. The team wants the metric to run during agent evaluation in Databricks and return a repeatable, structured score for each trace.
Which approach should the engineer use?

A. Log the traces to MLflow v3.x and review failures in the UI without defining a scorer.
B. Create a custom MLflow scorer that inspects agent outputs against the policy and pass it into the evaluation run.
C. Use only latency and token-count metrics because custom policy checks are not supported in evaluation workflows.
D. Add the policy text to the system prompt and rely on the model's self-reported compliance as the evaluation result.

正解：B

解説：
MLflow supports custom scorers for application-specific evaluation requirements that built-in metrics do not address. The engineer can define a scorer that examines the generated answer and, when necessary, the complete execution trace. It can verify references against an approved refund-rule list and check whether escalation statements violate the policy. The scorer returns structured feedback, such as a Boolean result, numerical score, or Feedback object, and is supplied to mlflow.genai.evaluate(). Deterministic checks are appropriate when repeatability is essential; semantic checks may require a carefully specified judge. Latency and token counts measure operational behavior rather than policy compliance. Reviewing traces manually does not create an automated metric, and the generating model's self-assessment is not an independent compliance evaluation. Databricks documentation

質問 # 38
A Generative AI Engineer has been asked to build an LLM-based question-answering application. The application should take into account new documents that are frequently published. The engineer wants to build this application with the least cost and least development effort and have it operate at the lowest cost possible.
Which combination of chaining components and configuration meets these requirements?

A. The LLM needs to be frequently with the new documents in order to provide most up-to-date answers.
B. For the application a prompt, an agent and a fine-tuned LLM are required. The agent is used by the LLM to retrieve relevant content that is inserted into the prompt which is given to the LLM to generate answers.
C. For the question-answering application, prompt engineering and an LLM are required to generate answers.
D. For the application a prompt, a retriever, and an LLM are required. The retriever output is inserted into the prompt which is given to the LLM to generate answers.

正解：D

解説：
Problem Context: The task is to build an LLM-based question-answering application that integrates new documents frequently with minimal costs and development efforts.
Explanation of Options:
* Option A: Utilizes a prompt and a retriever, with the retriever output being fed into the LLM. This setup is efficient because it dynamically updates the data pool via the retriever, allowing the LLM to provide up-to-date answers based on the latest documents without needing tofrequently retrain the model. This method offers a balance of cost-effectiveness and functionality.
* Option B: Requires frequent retraining of the LLM, which is costly and labor-intensive.
* Option C: Only involves prompt engineering and an LLM, which may not adequately handle the requirement for incorporating new documents unless it's part of an ongoing retraining or updating mechanism, which would increase costs.
* Option D: Involves an agent and a fine-tuned LLM, which could be overkill and lead to higher development and operational costs.
Option Ais the most suitable as it provides a cost-effective, minimal development approach while ensuring the application remains up-to-date with new information.

質問 # 39
A generative AI engineer is deploying an AI agent authored with MLflow's ChatAgent interface for a retail company's customer support system on Databricks. The agent must handle thousands of inquiries daily, and the engineer needs to track its performance and quality in real-time to ensure it meets service-level agreements. Which metrics are automatically captured by default and made available for monitoring when the agent is deployed using the Mosaic AI Agent Framework?

A. Quality metrics like correctness and guideline adherence
B. No metrics are automatically captured
C. Operational metrics like request volume, latency, and errors
D. Both operational and quality metrics

正解：C

解説：
When deploying an agent via the Mosaic AI Agent Framework (which leverages Databricks Model Serving), operational metrics are captured automatically by default. These include system-level telemetry such as the number of requests per second (volume), the time taken for the model to respond (latency), and the rate of 4xx/5xx HTTP errors. These are essential for monitoring Service Level Agreements (SLAs). However, Quality metrics (B), such as correctness, groundedness, or adherence to custom guidelines, cannot be determined "automatically" by the serving infrastructure because they require either human feedback or an LLM-as-a-judge evaluation (using Databricks Agent Evaluation). While Databricks makes it easy to generate quality metrics using the mlflow.evaluate API or the inference table, they are not "default operational metrics" that appear without additional evaluation configuration.

質問 # 40
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