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| Section | Weight | Objectives |
|---|---|---|
| Data Preparation | 14% | - Prepare data for RAG applications - Manage data quality and chunking |
| Evaluation and Monitoring | 12% | - Monitor deployed applications - Evaluate model quality and performance |
| Application Development | 30% | - Implement prompt engineering - Integrate Databricks tools (LangChain, etc.) - Develop LLM chains |
| Governance | 8% | - Apply safety filters and guidelines - Manage permissions and logging |
| Assembling and Deploying Apps | 22% | - Build and deploy RAG pipelines - Use Vector Search - Package and deploy models |
| Design Applications | 14% | - Select appropriate models - Define functional requirements - Design and architect applications |
>> 最新Databricks-Generative-AI-Engineer-Associate題庫 <<
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問題 #16
A Generative AI Engineer is developing an agent system using a popular agent-authoring library. The agent comprises multiple parallel and sequential chains. The engineer encounters challenges as the agent fails at one of the steps, making it difficult to debug the root cause. They need to find an appropriate approach to research this issue and discover the cause of failure. Which approach do they choose?
答案：D
解題說明：
For complex agentic systems (like those built with LangGraph or Autogen), standard logging is often insufficient because the "state" of the agent changes dynamically. MLflow Tracing is the designated Generative AI engineering standard for debugging these systems. Tracing provides a visual, hierarchical timeline of every call made during an agent's execution-including internal LLM reasoning, tool calls, and data transformations. When a step fails, the trace allows the engineer to click into that specific node to see the exact input sent to the LLM and the raw output received. This is much faster and more comprehensive than manually deconstructing the agent (D) or adding manual logs (C). While mlflow.evaluate (B) is useful for measuring performance across a whole dataset, it is not a tool for real-time debugging of a single execution failure.
問題 #17
A Generative AI Engineer is designing a chatbot for a gaming company that aims to engage users on its platform while its users play online video games.
Which metric would help them increase user engagement and retention for their platform?
答案：D
解題說明：
In the context of designing a chatbot to engage users on a gaming platform, diversity of responses (option B) is a key metric to increase user engagement and retention. Here's why:
Diverse and Engaging Interactions:
A chatbot that provides varied and interesting responses will keep users engaged, especially in an interactive environment like a gaming platform. Gamers typically enjoy dynamic and evolving conversations, and diversity of responses helps prevent monotony, encouraging users to interact more frequently with the bot.
Increasing Retention:
By offering different types of responses to similar queries, the chatbot can create a sense of novelty and excitement, which enhances the user's experience and makes them more likely to return to the platform.
Why Other Options Are Less Effective:
A (Randomness): Random responses can be confusing or irrelevant, leading to frustration and reducing engagement.
C (Lack of Relevance): If responses are not relevant to the user's queries, this will degrade the user experience and lead to disengagement.
D (Repetition of Responses): Repetitive responses can quickly bore users, making the chatbot feel uninteresting and reducing the likelihood of continued interaction.
Thus, diversity of responses (option B) is the most effective way to keep users engaged and retain them on the platform.
問題 #18
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?
答案：C
解題說明：
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.
問題 #19
A Generative AI Engineer is creating an agent-based LLM system for their favorite monster truck team. The system can answer text based questions about the monster truck team, lookup event dates via an API call, or query tables on the team's latest standings.
How could the Generative AI Engineer best design these capabilities into their system?
答案：B
解題說明：
In this scenario, the Generative AI Engineer needs to design a system that can handle different types of queries about the monster truck team. The queries may involve text-based information, API lookups for event dates, or table queries for standings. The best solution is to implement a tool-based agent system.
Here's how option B works, and why it's the most appropriate answer:
System Design Using Agent-Based Model:
In modern agent-based LLM systems, you can design a system where the LLM (Large Language Model) acts as a central orchestrator. The model can "decide" which tools to use based on the query. These tools can include API calls, table lookups, or natural language searches. The system should contain a system prompt that informs the LLM about the available tools.
System Prompt Listing Tools:
By creating a well-crafted system prompt, the LLM knows which tools are at its disposal. For instance, one tool may query an external API for event dates, another might look up standings in a database, and a third may involve searching a vector database for general text-based information. The agent will be responsible for calling the appropriate tool depending on the query.
Agent Orchestration of Calls:
The agent system is designed to execute a series of steps based on the incoming query. If a user asks for the next event date, the system will recognize this as a task that requires an API call. If the user asks about standings, the agent might query the appropriate table in the database. For text-based questions, it may call a search function over ingested data. The agent orchestrates this entire process, ensuring the LLM makes calls to the right resources dynamically.
Generative AI Tools and Context:
This is a standard architecture for integrating multiple functionalities into a system where each query requires different actions. The core design in option B is efficient because it keeps the system modular and dynamic by leveraging tools rather than overloading the LLM with static information in a system prompt (like option D).
Why Other Options Are Less Suitable:
A (RAG Architecture): While relevant, simply ingesting PDFs into a vector store only helps with text-based retrieval. It wouldn't help with API lookups or table queries.
C (Conditional Logic with RAG/API/TABLE): Although this approach works, it relies heavily on manual text parsing and might introduce complexity when scaling the system.
D (System Prompt with Event Dates and Standings): Hardcoding dates and table information into a system prompt isn't scalable. As the standings or events change, the system would need constant updating, making it inefficient.
By bundling multiple tools into a single agent-based system (as in option B), the Generative AI Engineer can best handle the diverse requirements of this system.
問題 #20
A Generative Al Engineer wants their (inetuned LLMs in their prod Databncks workspace available for testing in their dev workspace as well. All of their workspaces are Unity Catalog enabled and they are currently logging their models into the Model Registry in MLflow.
What is the most cost-effective and secure option for the Generative Al Engineer to accomplish their gAi?
答案：D
解題說明：
The goal is to make fine-tuned LLMs from a production (prod) Databricks workspace available for testing in a development (dev) workspace, leveraging Unity Catalog and MLflow, while ensuring cost-effectiveness and security. Let's analyze the options.
* Option A: Use an external model registry which can be accessed from all workspaces
* An external registry adds cost (e.g., hosting fees) and complexity (e.g., integration, security configurations) outside Databricks' native ecosystem, reducing security compared to Unity Catalog's governance.
* Databricks Reference:"Unity Catalog provides a centralized, secure model registry within Databricks"("Unity Catalog Documentation," 2023).
* Option B: Setup a script to export the model from prod and import it to dev
* Export/import scripts require manual effort, storage for model artifacts, and repeated execution, increasing operational cost and risk (e.g., version mismatches, unsecured transfers). It's less efficient than a native solution.
* Databricks Reference: Manual processes are discouraged when Unity Catalog offers built-in sharing:"Avoid redundant workflows with Unity Catalog's cross-workspace access"("MLflow with Unity Catalog").
* Option C: Setup a duplicate training pipeline in dev, so that an identical model is available in dev
* Duplicating the training pipeline doubles compute and storage costs, as it retrains the model from scratch. It's neither cost-effective nor necessary when the prod model can be reused securely.
* Databricks Reference:"Re-running training is resource-intensive; leverage existing models where possible"("Generative AI Engineer Guide").
* Option D: Use MLflow to log the model directly into Unity Catalog, and enable READ access in the dev workspace to the model
* Unity Catalog, integrated with MLflow, allows models logged in prod to be centrally managed and accessed across workspaces with fine-grained permissions (e.g., READ for dev). This is cost- effective (no extra infrastructure or retraining) and secure (governed by Databricks' access controls).
* Databricks Reference:"Log models to Unity Catalog via MLflow, then grant access to other workspaces securely"("MLflow Model Registry with Unity Catalog," 2023).
Conclusion: Option D leverages Databricks' native tools (MLflow and Unity Catalog) for a seamless, cost- effective, and secure solution, avoiding external systems, manual scripts, or redundant training.
問題 #21
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