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

Section	Objectives
Responsible AI and Governance	- AI safety and ethics 1. Bias and fairness considerations 2. Content safety filtering - Model governance 1. Access control and monitoring 2. Auditability and compliance
Retrieval-Augmented Generation (RAG)	- RAG architecture 1. Context retrieval and grounding 2. Document ingestion pipelines - Vector databases and embeddings 1. Embedding generation and similarity search 2. Vector index construction
Foundations of Generative AI	- Large Language Models (LLMs) 1. Transformer architecture basics 2. Tokenization and embeddings 3. Pretraining vs fine-tuning concepts - Generative AI Concepts 1. Hallucinations and mitigation strategies 2. Text generation principles
Databricks Mosaic AI Platform	- Model development and serving 1. Using Databricks Model Serving 2. Endpoint deployment concepts - MLflow for GenAI 1. Experiment tracking 2. Model registry usage
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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Databricks Certified Generative AI Engineer Associate Databricks-Generative-AI-Engineer-Associate Prüfungsfragen mit Lösungen (Q17-Q22):

17. Frage
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?

A. Run MLflow.evaluate to determine root cause of failed step.
B. Deconstruct the agent into independent steps to simplify debugging.
C. Enable MLflow tracing to gain visibility into each agent's behavior and execution step.
D. Implement structured logging within the agent's code to capture detailed execution information.

Antwort: C

Begründung:
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.

18. Frage
When developing an LLM application, it's crucial to ensure that the data used for training the model complies with licensing requirements to avoid legal risks.
Which action is NOT appropriate to avoid legal risks?

A. Only use data explicitly labeled with an open license and ensure the license terms are followed.
B. Reach out to the data curators directly after you have started using the trained model to let them know.
C. Use any available data you personally created which is completely original and you can decide what license to use.
D. Reach out to the data curators directly before you have started using the trained model to let them know.

Antwort: B

Begründung:
* Problem Context: When using data to train a model, it's essential to ensure compliance with licensing to avoid legal risks. Legal issues can arise from using data without permission, especially when it comes from third-party sources.
* Explanation of Options:
* Option A: Reaching out to data curatorsbeforeusing the data is an appropriate action. This allows you to ensure you have permission or understand the licensing terms before starting to use the data in your model.
* Option B: Usingoriginal datathat you personally created is always a safe option. Since you have full ownership over the data, there are no legal risks, as you control the licensing.
* Option C: Using data that is explicitly labeled with an open license and adhering to the license terms is a correct and recommended approach. This ensures compliance with legal requirements.
* Option D: Reaching out to the data curatorsafteryou have already started using the trained model isnot appropriate. If you've already used the data without understanding its licensing terms, you may have already violated the terms of use, which could lead to legal complications. It's essential to clarify the licensing termsbeforeusing the data, not after.
Thus,Option Dis not appropriate because it could expose you to legal risks by using the data without first obtaining the proper licensing permissions.

19. Frage
A Generative Al Engineer would like an LLM to generate formatted JSON from emails. This will require parsing and extracting the following information: order ID, date, and sender email. Here's a sample email:

They will need to write a prompt that will extract the relevant information in JSON format with the highest level of output accuracy.
Which prompt will do that?

A. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.
Here's an example: {"date": "April 16, 2024", "sender_email": "sarah.lee925@gmail.com", "order_id":
"RE987D"}
B. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in JSON format.
C. You will receive customer emails and need to extract date, sender email, and order ID. Return the extracted information in a human-readable format.
D. You will receive customer emails and need to extract date, sender email, and order ID. You should return the date, sender email, and order ID information in JSON format.

Antwort: A

Begründung:
Problem Context: The goal is to parse emails to extract certain pieces of information and output this in a structured JSON format. Clarity and specificity in the prompt design will ensure higher accuracy in the LLM' s responses.
Explanation of Options:
* Option A: Provides a general guideline but lacks an example, which helps an LLM understand the exact format expected.
* Option B: Includes a clear instruction and a specific example of the output format. Providing an example is crucial as it helps set the pattern and format in which the information should be structured, leading to more accurate results.
* Option C: Does not specify that the output should be in JSON format, thus not meeting the requirement.
* Option D: While it correctly asks for JSON format, it lacks an example that would guide the LLM on how to structure the JSON correctly.
Therefore,Option Bis optimal as it not only specifies the required format but also illustrates it with an example, enhancing the likelihood of accurate extraction and formatting by the LLM.

20. Frage
A Generative AI Engineer developed an LLM application using the provisioned throughput Foundation Model API. Now that the application is ready to be deployed, they realize their volume of requests are not sufficiently high enough to create their own provisioned throughput endpoint. They want to choose a strategy that ensures the best cost-effectiveness for their application.
What strategy should the Generative AI Engineer use?

A. Deploy the model using pay-per-token throughput as it comes with cost guarantees
B. Switch to using External Models instead
C. Throttle the incoming batch of requests manually to avoid rate limiting issues
D. Change to a model with a fewer number of parameters in order to reduce hardware constraint issues

Antwort: A

Begründung:
* Problem Context: The engineer needs a cost-effective deployment strategy for an LLM application with relatively low request volume.
* Explanation of Options:
* Option A: Switching to external models may not provide the required control or integration necessary for specific application needs.
* Option B: Using a pay-per-token model is cost-effective, especially for applications with variable or low request volumes, as it aligns costs directly with usage.
* Option C: Changing to a model with fewer parameters could reduce costs, but might also impact the performance and capabilities of the application.
* Option D: Manually throttling requests is a less efficient and potentially error-prone strategy for managing costs.
OptionBis ideal, offering flexibility and cost control, aligning expenses directly with the application's usage patterns.

21. Frage
An AI developer team wants to fine-tune an open-weight model to have exceptional performance on a code generation use case. They are trying to choose the best model to start with. They want to minimize model hosting costs and are using Hugging Face model cards and spaces to explore models. Which TWO model attributes and metrics should the team focus on to make their selection?

A. Big Code Models Leaderboard
B. MTEB Leaderboard
C. Number of model downloads last month
D. Number of model parameters
E. Chatbot Arena Leaderboard

Antwort: A,D

Begründung:
To optimize for code generation performance and hosting costs , a Generative AI engineer must look at specific metrics.
* Big Code Models Leaderboard (A): This is the industry-standard benchmark for code-specific LLMs (like StarCoder or CodeLlama). It measures performance on tasks like HumanEval and MBPP, providing a direct indicator of how well the model handles programming logic.
* Number of model parameters (B): This is the primary driver of hosting costs. Larger models (e.g.,
70B) require more GPU memory (VRAM) and more expensive compute instances (like A100s/H100s) than smaller models (e.g., 7B or 13B). To minimize costs, the team should look for the smallest model that achieves a high score on the Big Code Leaderboard.
Note: MTEB (C) is for embeddings, and Chatbot Arena (D) is for general-purpose chat, neither of which is the primary metric for specialized code generation fine-tuning.

22. Frage
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