Databricks Certified Generative AI Engineer Associate Sample Questions - 105922 ( 2026 )

Ace the new Databricks Certified Generative AI Engineer Associate exam with our handpicked Databricks Generative AI Engineer Associate Sample Questions designed around the latest 105922 certification format. Whether you're reviewing Databricks Certified Generative AI Engineer Associate Practice Tests, going through 105922 Dumps, or solving real exam questions, this resource helps you master GenAI fundamentals, LLM optimization, prompt engineering, and real-world AI use cases. Ideal for professionals looking to validate their GenAI skills with the Databricks ecosystem and achieve certification success in 2026.

Databricks Certified Generative AI Engineer Associate Sample Questions List :

1. An LLM-based agent will use tools such as calculators and web search to complete tasks. What’s the best way to expose these functions to the model?

1. Use the following tools: Tool1, Tool2, Tool3.'

2. Avoid using tools.'

3. Use the internet.'

4. Answer freely.'

2. A team is setting up the model lifecycle for a new AI assistant. They want to distinguish between pre-deployment checks and ongoing live system tracking. How should they compare evaluation and monitoring?

1. Monitoring is before deployment

2. Evaluation uses real data

3. Monitoring is only for QA teams

4. Monitoring tracks live performance; evaluation checks pre-deployment behavior

3. An engineer is coding a simple RAG application that requires document retrieval, prompt construction, and generation. What is the minimum set of components needed to complete this flow?

1. Retriever → Prompt Template → LLM

2. Prompt → Embedding → Generator

3. Vector index → Classifier → JSON

4. Retriever → Tokenizer → Memory

4. An LLM-powered customer support assistant is live in production. The team wants to ensure reliability and responsiveness. Which metrics should they monitor?

1. Retrieval chunk size

2. Prompt engineering time

3. Output latency and error rate

4. User hobbies

5. A Generative AI Engineer is developing a model-serving endpoint that needs to validate and format user inputs before passing them to the model, and also adjust the model’s outputs before returning them to the client. Which technique supports this requirement?

1. Pyfunc model with pre- and post-processing

2. Tokenizer settings

3. Prompt chaining

4. Embedding model

6. A legal tech company is launching a document review assistant powered by LLMs. To ensure trust and traceability, what should they implement for each model inference?

1. To extend vector lifetime

2. To improve prompt chunking

3. To delay outputs

4. To capture hallucinations and safety violations

7. A data scientist is ready to productionize their LLM by registering it to Unity Catalog using MLflow. What MLflow function allows this?

1. mlflow.create_table()

2. spark.saveModel()

3. model.to_delta()

4. mlflow.register_model("runs:/<run_id>/model", "catalog.schema.name")

8. A developer using LangChain needs to bind a prompt to a specific LLM to enable basic interactions in their application. Which class should they use?

1. MemoryChain

2. LLMChain

3. ChunkCombiner

4. PromptWrapper

9. A developer wants to allow an LLM to query an external weather API during a user interaction. The LLM should decide when and how to call the API dynamically. Which LangChain component enables this behavior?

1. VectorStoreRetriever

2. PromptTemplate

3. AgentExecutor

4. LLMChain

10. A customer service chatbot based on RAG fails to provide answers about refunds. Upon investigation, the engineer discovers the refund policy isn't part of the indexed knowledge base. What document should be added to improve the application?

1. HR handbook

2. Product manuals

3. Press releases

4. Refund policy document

11. A developer is working with scanned PDFs that contain text in image format. To convert the content for downstream embedding and indexing, they need to extract readable text from these files. Which Python library should they use?

1. PyPDF2

2. pytesseract

3. openai

4. pdfminer

12. A product team is designing a tool that transforms lengthy user-generated reviews into concise one-sentence insights that can be displayed on product pages. Which task should the team select when choosing a model for this function?

1. Keyword Extraction

2. Text Classification

3. Summarization

4. Sentiment Analysis

13. An engineer is preparing training examples for a summarization task and must choose suitable prompt/response pairs. Which example is most appropriate to fine-tune a model on summarizing customer reviews?

1. Prompt: 'Classify the tone' → Response: 'Positive'

2. Prompt: 'Summarize this review' → Response: 'Excellent quality, fast shipping'

3. Prompt: 'Rewrite this' → Response: 'Same content'

4. Prompt: 'What is this?' → Response: 'Good'

14. A data engineer is setting up a Retrieval-Augmented Generation (RAG) pipeline where user queries must be matched to source documents, restructured into prompts, and then passed to an LLM. What is the correct sequence of components for this pipeline?

1. Retriever → Prompt Template → LLM

2. Prompt → Retriever → LLM

3. Retriever → LLM → Output Formatter

4. LLM → Retriever → Prompt

15. A user submitted feedback stating that the model’s answers were accurate but sounded rude. What issue should the Generative AI Engineer investigate?

1. Tone/safety concern

2. Token overflow

3. Chunk overlap

4. Retrieval error

16. A team is comparing two summarization models. One model shows a significantly higher ROUGE-L score. What can they conclude?

1. It’s less accurate

2. It’s longer

3. It’s worse at classification

4. It more closely matches human summaries

17. A Generative AI Engineer has been tasked with developing a pipeline to identify and redact personally identifiable names from legal contracts. What is the most appropriate underlying NLP task to accomplish this?

1. Text Generation

2. Named Entity Recognition

3. Summarization

4. Topic Modeling

18. An engineer needs to implement semantic search on a Databricks Vector Search index to retrieve contextually similar chunks for generation. What command should be used?

1. ai_query()

2. SELECT * FROM index

3. VECTOR_SEARCH()

4. DELTA RETRIEVE

19. An engineer is preparing a document set for a RAG-based assistant. During review, they notice that each page contains redundant disclaimers in the header and footer. What preprocessing step should be taken to improve application quality?

1. Remove repetitive blocks during preprocessing

2. Keep everything

3. Increase token size

4. Use a different LLM

20. A developer plans to embed document chunks that are 1500 tokens long. What’s the minimum context length their embedding model should support?

1. 256 context tokens

2. 128 tokens

3. 512 tokens

4. 2048 tokens

21. The output of a model summarizing product reviews is technically correct but sounds flat and uninspiring. How should the prompt be modified to generate more persuasive text?

1. Add emotional appeal to the review.'

2. Change tone.'

3. Summarize.'

4. Make it longer.'

22. An engineer is developing a logistics assistant that returns estimated arrival dates. They want to ensure the date format is always MM/DD/YYYY to match internal systems. What type of prompt should they use to enforce this?

1. What’s the delivery date?

2. Estimate arrival.

3. When is it coming?

4. Provide the expected arrival date in MM/DD/YYYY format.

23. A hospital is deploying a summarization model to generate clinical summaries from physician notes. The deployment team is focused on ensuring the outputs are factually correct. Which evaluation metric should they prioritize?

1. Perplexity

2. BLEU

3. Latency

4. Factual consistency

24. An engineer is reviewing queries submitted to a chatbot and finds attempts like 'how to hack a website.' To prevent such prompts from being processed, what feature should they implement?

1. Intent classifier to block unsafe inputs

2. Prompt delay

3. Sentiment filter

4. Prompt reformatter

25. A data engineer has chunked and processed raw text from corporate documents and now wants to persist the chunks for fast retrieval in a governed data environment. What is the best approach to store this data?

1. Use MLflow directly

2. Write as Delta tables in Unity Catalog

3. Log to notebook

4. Save to CSV

26. A team is evaluating LLMs for a customer support chatbot that must operate in multiple languages. Which model attribute is most critical?

1. Model size

2. Trained on multilingual corpora

3. Pretrained on math

4. Number of citations

27. A machine learning team observes that their model is memorizing names and sensitive personal data from training documents. What should they do to reduce this overfitting and improve privacy?

1. Mask personal identifiers

2. Use more data

3. Train longer

4. Add more prompts

28. A team wants to prototype an LLM solution without managing model infrastructure. They decide to use Databricks-hosted models. What service should they leverage?

1. To train models

2. To serve LLMs without managing infrastructure

3. To replace Unity Catalog

4. To embed documents

29. A legal tech startup is creating an AI agent that will process lengthy legal contracts, check them against internal compliance policies, and summarize findings into a report. What is the correct sequence of tools the engineer should integrate?

1. Retriever → Prompt → Output Parser

2. Classifier → Generator → Filter

3. Formatter → LLM → Output Selector

4. Document Parser → Policy Comparator → LLM Generator

30. A Generative AI Engineer is using MLflow in a RAG pipeline to manage prompt templates, LLM configs, and evaluation data. What is the key benefit of MLflow in this scenario?

1. Prompt delay management

2. Chunk storage

3. Inference pipeline tracking and versioning

4. GPU scaling

31. An engineering team is tasked with summarizing thousands of documents overnight using a scheduled pipeline. Which serving approach should they use?

1. Retrieval reranking

2. Bulk summarization of documents

3. Real-time chatbot

4. Live Q&A

32. An enterprise plans to embed content from a premium news provider into their internal LLM knowledge base for employee access. What must the team do before proceeding?

1. Use a smaller model

2. Check the licensing terms before use

3. Ask ChatGPT

4. Embed it freely

33. A Generative AI Engineer is tasked with indexing a large document corpus into a vector database that has a strict upper limit on record count. The current setup produces too many chunks for the system to store. Which adjustment should the engineer make?

1. Increase chunk size

2. Decrease chunk overlap

3. Randomize chunk order

4. Use smaller embeddings

34. A developer is building a retrieval system using an LLM with a limited context window of 512 tokens. What chunking approach will optimize accuracy and avoid truncation?

1. Entire document per chunk

2. 1000 tokens with 50% overlap

3. 256 tokens, minimal overlap

4. 2048 tokens

35. A team is building a RAG-based assistant using internal documents. During ingestion, they notice some files contain profanity. How should they address this before indexing?

1. Use larger chunks

2. Add disclaimers

3. Increase temperature

4. Mask profane terms before indexing

36. An AI developer is building a model to prioritize incoming emails by urgency. The model needs to output categories like 'urgent', 'low', or 'normal.' What is the most appropriate description of the desired model output?

1. Full email content

2. Shortened text

3. Topic summary

4. A single label from the three categories

37. An enterprise is deploying a hosted LLM on Databricks and wants to ensure only authorized employees from specific business units can access the model. What security configuration should be implemented?

1. Hardcoded IP check

2. Public API key

3. OAuth redirect

4. Unity Catalog permissions or token-based control

38. A Generative AI Engineer is designing a RAG application and needs to decide which components are required. Which of the following is not a necessary component?

1. Retriever

2. Embedding model

3. Prompt Template

4. Reinforcement Learning Trainer

39. A developer is outlining the deployment steps for a new RAG application. What is the correct sequence to bring the app from chunked data to a live endpoint?

1. Prompt → Embed → Retrieve → Train

2. Retrieve → Train → Serve

3. Save → Upload → Embed

4. Embed → Chunk → Retrieve → Prompt → Serve

40. A customer asks a support bot, “Where’s my order?” The engineer wants the system to give personalized responses. What augmentation should be included in the prompt?

1. Append their last 3 order statuses

2. Skip augmentation

3. Nothing

4. Add a product image

41. Before integrating a summarization model from Hugging Face into a production pipeline, what should the engineer review?

1. Tokenizer type

2. API name

3. Number of stars

4. Training data and evaluation benchmarks

42. A Generative AI Engineer is tuning prompts to avoid hallucinations in a finance assistant. What is the best directive to include in the prompt?

1. Be creative.'

2. Only respond if you are certain. Say I don't know otherwise.'

3. Make up details when unsure.'

4. Always return something.'

43. A content strategist is working on a system to automatically generate catchy blog headlines. The requirement is for these headlines to be under 10 words and written in title case. Which prompt format should they use to consistently elicit the desired output?

1. Provide a headline in under 10 words with title case

2. List important topics

3. Summarize the post

4. Extract key phrases

44.  A data engineer is building a pipeline to retrieve internal documents hosted on SharePoint for use in a RAG application. Which document loader should they choose to extract the contents?

1. CSVLoader

2. PyPDFLoader

3. SharePointLoader

4. JSONLoader

45. A QA team wants to prevent a model from responding to unethical or illegal prompts. What feature should be added to the GenAI application to enforce this?

1. Increase prompt length

2. Use an intent classifier to reject malicious queries

3. Lower temperature

4. Log the response only

46. What mechanism ensures that AI-generated outputs do not expose sensitive information?

1. Removing embeddings from metadata

2. Data masking and differential privacy

3. Increasing inference speed

4. Disabling prompt chaining

47. What is a key advantage of using document chunking in AI applications?

1. It prevents embedding creation

2. It optimizes retrieval efficiency and improves response accuracy

3. It reduces model training costs

48. What is the primary function of Databricks Model Serving?

1. Reducing model training times

2. Deploying and managing ML models in a scalable environment

3. Handling metadata extraction for RAG

4. Eliminating the need for LLM chaining

49. What is the role of prompt augmentation in AI applications?

1. To reduce model size

2. To disable embeddings

3. To remove irrelevant words

4. To provide additional context to improve LLM responses

50. What is the purpose of using an LLM judge for AI evaluation?

1. To ensure all responses are deterministic

2. To assess the correctness and groundedness of AI-generated responses

3. To increase the token limit of responses

4. To replace human evaluation entirely

51. How can one handle real-time updates to vector search databases?

1. Use periodic index rebuilding or incremental updates

2. Delete all old indexes before adding new ones

3. Reduce the number of embeddings

4. Avoid updating the index

52. What is the primary advantage of using a cloud-based deployment for LLM applications?

1. Eliminates all inference costs

2. Scalability and access to managed infrastructure

3. Enables offline model execution

4. Removes the need for vector search

53. What is a major advantage of using Databricks Model Serving for LLM applications?

1. It enables seamless API-based deployment of models

2. It allows zero-latency inference

3. It ensures all responses are deterministic

4. It eliminates the need for embedding models

54. What role does Unity Catalog play in AI model governance?

1. Eliminates the need for embeddings

2. Reduces model training costs

3. Provides LLM-based inference APIs

4. Provides access control and tracking for AI assets

55. What is the role of re-ranking in retrieval systems?

1. Lowers token consumption

2. Reduces API response times

3. Avoids hallucinations entirely

4. Prioritizes the most relevant retrieved results

56. What is the purpose of vector embeddings in a retrieval system?

1. To store user queries in plaintext

2. To convert text into numerical representations for semantic search

3. To increase token consumption

4. To eliminate the need for indexing

57. Why is load balancing important when deploying AI models in production?

1. Increases the cost of API calls

2. Reduces model accuracy

3. Distributes requests evenly to prevent system overload

4. Prevents data retrieval

58. What is the primary purpose of prompt engineering in generative AI applications?

1. To improve the clarity and effectiveness of model responses

2. To limit the number of API calls

3. To reduce the size of the model

4. To increase model training speed

59. Why is latency monitoring important in production AI systems?

1. To eliminate the need for vector search

2. To ensure real-time performance and user experience

3. To reduce cloud computing costs

4. To increase LLM inference complexity

60. Why is it important to track model lineage in enterprise AI applications?

1. To eliminate model drift

2. To reduce model serving costs

3. To avoid API key exposure

4. To ensure compliance and reproducibility

61. What role does MLflow play in the lifecycle of an LLM application?

1. Eliminates the need for embeddings

2. Removes the need for GPU inference

3. Compresses prompts for cost reduction

4. Provides model versioning, experiment tracking, and evaluation metrics

62. How does tokenization impact LLM performance?

1. It increases hallucinations

2. It reduces API response time

3. It determines how text is split and processed by the model

4. It eliminates the need for fine-tuning

63. How can you register a trained model in MLflow for deployment?

1. mlflow.deploy_model("my_model")

2. mlflow.register_model(model_uri, name="my_model")

3. mlflow.configure_model("my_model")

4. mlflow.upload_model("my_model")

64. How can AI agents decide whether to use a retrieval tool in a RAG application?

1. By disabling vector search

2. By analyzing the user query and determining if external context is required

3. By always using retrieval regardless of query type

4. By randomly selecting a retrieval tool

65. What is the benefit of containerizing an AI model before deployment?

1. Ensures environment consistency and portability

2. Reduces the number of API calls

3. Increases inference latency

4. Eliminates the need for monitoring

66. Which factor should be considered when selecting a chunking strategy?

1. Reducing chunk overlap to zero

2. Model context window size

3. Ignoring document structure

4. Randomized chunking approaches

67. Which approach is best for handling multi-turn conversations in an AI chatbot?

1. Disabling embeddings

2. Reducing response length to a single sentence

3. Storing previous responses and using context tracking

4. Increasing model temperature

68. Why is metadata extraction important for RAG-based applications?

1. It enhances search filtering and retrieval accuracy

2. It reduces API latency

3. It eliminates the need for chunking

4. It increases model inference speed

69. How can retrieval performance be improved in a RAG-based application?

1. Reducing document chunking size to a single sentence

2. Eliminating metadata filtering

3. Removing embeddings from vector search

4. Using hybrid search techniques

70. What is the advantage of using Databricks MLflow for model deployment?

1. Reduces cloud storage costs

2. Tracks model performance, lineage, and deployment status

3. Disables LLM fine-tuning

4. Eliminates inference failures

FAQs

1. What is the Databricks Certified Generative AI Engineer Associate certification?

The Databricks Certified Generative AI Engineer Associate certification validates your ability to design, develop, and deploy generative AI applications using Databricks tools. It focuses on core GenAI skills like LLM chaining, prompt engineering, vector search, governance using Unity Catalog, and model deployment with MLflow.

2. Is Databricks Certified Generative AI Engineer Associate worth it?

Yes, this certification is highly valuable if you're working in AI, data engineering, or MLOps. It demonstrates your ability to implement GenAI-powered solutions using Databricks, making you more competitive in the job market.

3. What are the prerequisites for Databricks Certified Generative AI Engineer Associate?

There are no strict prerequisites, but it is recommended to have: 

• Basic Python programming skills

• Experience with LLM applications

• Familiarity with LangChain or similar libraries

• Hands-on experience with Databricks tools like MLflow, Vector Search, and Unity Catalog

4. What is the format of the Databricks Generative AI Engineer Associate exam?

The exam consists of multiple-choice questions, most of which are scenario-based. Some questions involve code snippets or architecture design.

5. How many questions are on the Databricks Generative AI certification exam?

The exam includes 45 scored multiple-choice questions and a few unscored items. You have 90 minutes to complete it.

6. What kind of questions are asked in the Databricks Generative AI Engineer exam?

You can expect questions related to:

• Prompt engineering techniques

• Model evaluation and testing

• LLM pipelines (e.g., LangChain)

• RAG (Retrieval-Augmented Generation)

• Vector database usage and optimization

7. Does the Databricks Generative AI Engineer exam include coding questions?

Yes, some questions may include Python code snippets, but you don’t have to write code. Instead, you analyze or interpret existing code.

8. What programming languages are used in the Databricks Generative AI exam?

The questions primarily use Python. Familiarity with basic Python and frameworks like LangChain is helpful.

9. What topics are covered in the Databricks Generative AI Engineer Associate exam?

The exam covers six core domains:

• Application Design (14%)

• Data Preparation (14%)

• Application Development (30%)

• Assembling and Deploying Apps (22%)

• Governance (8%)

• Evaluation and Monitoring (12%)

10. What is the cost of the Databricks Certified Generative AI Engineer Associate exam?

The registration fee is $200 USD. This includes one attempt at the certification exam.

11. How do I register for the Databricks Certified Generative AI Engineer Associate exam?

You can register via the official Databricks certification portal. Choose your preferred language, pay the fee, and schedule your exam online.

12. Can I take the Databricks Generative AI Engineer Associate exam online?

Yes, the exam is available online through a secure proctored environment. You need a webcam, microphone, and a quiet room.

13. How hard is the Databricks Generative AI Engineer Associate exam?

The exam is moderately challenging. If you have hands-on experience with LLMs and Databricks tools, and you study the recommended resources, it’s manageable.

14. What is the passing score for the Databricks Generative AI certification?

Databricks does not officially disclose the passing score, but most candidates report that you need around 70% to pass.

15. How do I prepare for the Databricks Certified Generative AI Engineer Associate exam?

Here are some tips:

• Complete the Databricks training course: "Generative AI Engineering with Databricks"

• Practice using LangChain, MLflow, and Unity Catalog

• Use Databricks Community Edition for hands-on labs

• Take mock tests and review sample questions

16. Are there any free resources to study for the Databricks Generative AI exam?

Yes, Databricks offers free resources including:

• Documentation and blog posts

• Sample notebooks and tutorials

• Free access to the Community Edition for practical exercises

17. Is there any official training for the Databricks Generative AI Engineer certification?

Yes, Databricks offers an official training course called "Generative AI Engineering with Databricks," which covers all exam topics in depth.

18. How long does it take to prepare for the Databricks Certified Generative AI exam?

Preparation time depends on your background. On average, 2 to 4 weeks of focused study (1-2 hours daily) is sufficient for most candidates.

19. How long is the Databricks Certified Generative AI Engineer Associate valid?

The certification is valid for 2 years. After that, you must retake the current version of the exam to stay certified.

20. How to get Databricks Generative AI Engineer Associate certification?

Follow these steps:

1. Review the exam guide and topics

2. Prepare with hands-on practice and training

3. Register on the Databricks website

4. Take the online proctored exam

5. Score above the passing mark to receive your certificate