Databricks GenAI Engineer Associate Exam Part 1 - Quiz
A medium-difficulty quiz on the exam structure, study plan, and scenario-based reasoning from the Databricks GenAI Engineer Associate walkthrough.
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Quiz Questions & Answers
Review every prompt, the correct responses, and helpful context to prep for your own run-through.
Question 1: What is the exam mainly testing when it gives you a real-world scenario?
Scenario-based judgment under constraints
Writing long code from memory
Pure Python syntax recall
Only memorizing model names
Question 2: Which study plan structure matches the walkthrough?
An open-ended six-month project
A focused 4-week roadmap
A one-day cram session
A 12-week theory-only course
Question 3: In the house analogy, what does the blueprint phase represent?
Packaging models for serving
Monitoring live endpoints
Masking sensitive data
Turning business needs into clear AI inputs and outputs
Question 4: When preparing materials, what should you do before chunking documents?
Remove repeated headers and footers
Increase the model temperature
Fine-tune the generator immediately
Skip preprocessing to save time
Question 5: Which skill belongs most clearly to application development in week two?
Working with chains and agentic systems
Applying data masking policies
Choosing legal retention rules
Tracking inference tables
Question 6: What is the best fit for the assembling and deploying phase?
Registering models and serving them with Databricks tools
Translating business needs into prompts
Removing noisy document headers
Choosing the right legal licenses
Question 7: A production GenAI app needs strict oversight and rules. Which week focuses on that mindset?
Week two
Week four
Week one
Week three
Question 8: For governance, which action best matches the exam guidance?
Mask sensitive data and apply guardrails
Avoid documenting data sources
Use manual token counting
Increase output randomness
Question 9: You need to track endpoint usage, apply rate limits, and keep logs for analysis. What should you use?
AI Gateway with inference tables
Manual spreadsheet tracking
A larger model with no logging
Disabling endpoint logs
Question 10: When a RAG app returns noisy chunks with headers and footers, what should you fix first?
Switch to a fine-tuned generator immediately
Clean the data before chunking and embedding
Raise the temperature for more variety
Ignore the noise and deploy anyway