Databricks GenAI Engineer Sample Questions With Full Explanations
Here are 8 multiple-choice questions designed to mirror the difficulty, style, and domain coverage of the Databricks GenAI Engineer Associate exam. They focus on practical implementation, architecture patterns, and evaluation—prioritizing how well you can apply Databricks tools (MLflow, Vector Search, Feature Serving, Model Serving) to real GenAI use cases.
Instructions: Choose the best answer for each question. Pay close attention to the specific wording, as the exam often tests nuanced differences between similar concepts (e.g., prompt caching vs. fine-tuning, or Delta Tables vs. Vector Search). For more on this topic, see our guide on AWS Data Engineer Associate Sample Questions With Full Explanations.
Question 1
A data scientist is building a RAG application using Databricks. They need to store and retrieve unstructured text documents (e.g., PDFs) based on semantic similarity. Which Databricks feature is most appropriate for this specific task?
A. Delta Lake
B. Feature Store
C. Vector Search
D. Model Serving
Answer: C
Explanation: Vector Search is purpose-built for semantic similarity search over embeddings, which is the core of RAG retrieval. Delta Lake is for structured tabular data, the Feature Store is for managing ML features, and Model Serving is for hosting models, not for storing documents.
Question 2
A team has developed a fine-tuned LLM and logged it to MLflow. They want to deploy it to a production endpoint that can scale to zero when not in use and provide a RESTful API. Which serving approach should they use?
A. Foundation Model API
B. Provisioned Throughput Serving
C. Custom Model Serving with a GPU endpoint
D. Batch Inference via a Delta Live Table pipeline
Answer: C
Explanation: Custom Model Serving allows you to deploy a custom fine-tuned model (e.g., a PyFunc or a transformer) with autoscaling, including scaling to zero. The Foundation Model API serves pre-hosted models, Provisioned Throughput is for high-performance fixed capacity, and batch inference is for offline processing.
Question 3
During the development of a chatbot, you notice the model frequently "hallucinates" facts about your company's internal policies. You have a curated set of policy documents. What is the most effective strategy to reduce these hallucinations?
A. Increase the model's temperature parameter to 0.0.
B. Use a RAG architecture with Vector Search and cite retrieved sources.
C. Fine-tune the base model on a large dataset of general knowledge.
D. Use a larger base model with more parameters. If you're preparing for this exam, AWS Machine Learning Engineer Sample Questions With Full Explanations is essential reading.
Answer: B
Explanation: RAG grounds the model's responses in retrieved, authoritative context, providing evidence and reducing fabrication. Lowering temperature helps but doesn't add knowledge, fine-tuning on general knowledge is irrelevant, and a larger model may still hallucinate without the correct context.
Question 4
You are evaluating two different prompt templates for a summarization task. You want to measure the semantic similarity between the generated summaries and a set of reference summaries. Which MLflow metric is specifically designed for this purpose?
A. mlflow.metrics.toxicity
B. mlflow.metrics.rouge
C. mlflow.metrics.bleu
D. mlflow.metrics.flesch_kincaid_grade_level
Answer: B
Explanation: ROUGE is a standard metric for summarization that compares overlapping n-grams and word sequences. Toxicity measures harmful content, BLEU is more common for machine translation, and Flesch-Kincaid measures readability, not semantic quality.
Question 5
A GenAI application requires real-time access to a customer's credit score to personalize a loan offer. The credit score is calculated by a separate, frequently-updated machine learning model. What is the best practice for providing this feature to your LLM application?
A. Embed the credit score directly in the prompt as static text.
B. Serve the credit score model via Feature Serving and call it from your GenAI app.
C. Store the credit scores in a Delta table and query it with Spark.
D. Re-train the LLM to memorize the credit score model's logic.
Answer: B
Explanation: Feature Serving provides low-latency access to computed features (like credit scores) for online inference, ensuring the LLM gets fresh, accurate data. Embedding static text is stale, Delta tables are too slow for real-time, and re-training an LLM to compute scores is impractical and error-prone.
Question 6
You are building an agent that uses an LLM to answer questions about internal code. The agent needs to call a Python function that executes code and returns the output. In the context of LangChain and Databricks, what is this function called?
A. A vector store
B. A prompt template
C. A tool Looking for more detail? See Google Cloud DevOps Engineer Sample Questions With Full Explanations.
D. A retriever
Answer: C
Explanation: In agent architectures (like LangChain), external functions that the LLM can invoke are called "tools." A vector store is for retrieval, a prompt template formats the input, and a retriever fetches documents.
Question 7
Your team is using MLflow to track experiments for a prompt-tuning task. You want to log the specific prompt template, the model parameters, and the resulting evaluation metrics for each run. What is the correct MLflow API call to log these artifacts?
A. mlflow.log_model()
B. mlflow.log_param() and mlflow.log_metric()
C. mlflow.register_model()
D. mlflow.serve()
Answer: B
Explanation: log_param is for key-value pairs of inputs (prompt template, temperature), and log_metric is for evaluation results (e.g., ROUGE score). log_model registers the model artifact, register_model moves it to the registry, and serve deploys it.
Question 8
When deploying a RAG application on Databricks Model Serving, you need to ensure that the endpoint can handle a spike in traffic without returning errors, while also minimizing cost during idle periods. Which configuration best meets these requirements?
A. Set the endpoint to "Provisioned Throughput" with a fixed high concurrency.
B. Enable autoscaling with a minimum of 0 and a maximum of 8 concurrent requests.
C. Deploy the model on a single-node cluster with a large driver.
D. Use a serverless SQL warehouse for the inference calls.
Answer: B
Explanation: Autoscaling with a minimum of 0 (scale-to-zero) handles idle periods cost-effectively, while the maximum limit allows it to scale up to absorb traffic spikes. Provisioned Throughput is for guaranteed performance but is always-on and costly, a single node is a single point of failure, and SQL warehouses are for data queries, not model inference.
These questions cover key areas like retrieval, deployment, evaluation, and agent architecture. Remember that the real exam heavily emphasizes the integration of these components—e.g., how to connect Vector Search to Model Serving, or how to use MLflow to evaluate a RAG chain.
The best way to solidify these concepts is to practice with realistic scenarios.
Before you start those scenarios, take the free Databricks Certified Generative AI Engineer Associate readiness check to get a quick read on your weak topics.
Give your preparation its final stress test with full-length Databricks Certified Generative AI Engineer Associate mock tests before the real exam.
If you want a week-by-week schedule that fits your available hours, use the free Databricks Certified Generative AI Engineer Associate study plan to map out your prep around the topics that matter most.