Databricks GenAI Engineer Associate
Free Databricks Certified GenAI Engineer Associate Practice Test
12 exam-style questions with full explanations — no sign-up. Score yourself, then close your gaps with the full course.
Exam fee ~$2006 exam domainsLevel Intermediate2 timed practice tests in the course
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A company is building a RAG application that must retrieve context from 50,000 technical documentation PDFs. The application needs to handle 1000 concurrent users with sub-second response times. The team wants to minimize infrastructure management while maintaining high retrieval accuracy. Which architecture BEST meets these requirements?
Databricks Vector Search is specifically designed for production RAG applications at scale. It provides managed vector indexing with Delta Sync, automatic embedding updates, and sub-second retrieval. The serving endpoint handles auto-scaling for concurrent users, and semantic search with filters enables accurate retrieval from large document sets. This is the only option that meets all requirements without custom infrastructure management.
A data engineering team is preparing a dataset of customer support transcripts for fine-tuning a large language model. The raw text data contains personally identifiable information (PII) including email addresses, phone numbers, and social security numbers that must be removed before training. Which Databricks feature should they use to systematically detect and redact PII while maintaining the conversational context of the transcripts?
Unity Catalog provides built-in PII detection through data profiling capabilities that can automatically identify sensitive data patterns across multiple PII types. This integrates with governance policies to systematically handle PII at scale while preserving data utility for model training.
A GenAI application needs to process user queries and generate responses using a foundation model deployed on Databricks. The application must track token usage, latency, and error rates for each request to monitor costs and performance. Which approach BEST implements this monitoring requirement?
Inference tables are the native Databricks Model Serving feature specifically designed to automatically capture request/response data, timestamps, and metadata including token usage for LLM endpoints. They provide immediate queryability through Delta tables without requiring custom implementation, making them the most efficient and reliable solution for monitoring GenAI applications.
A data science team has developed a RAG application using LangChain and needs to deploy it to production with versioning, rollback capabilities, and endpoint monitoring. The application uses a Vector Search index and a foundation model from the Foundation Model API. Which deployment approach BEST meets these requirements?
MLflow models registered with the RAG chain as a pyfunc model can be deployed directly to Databricks Model Serving endpoints, which provide built-in versioning, A/B testing, rollback capabilities, automatic scaling, and comprehensive endpoint monitoring. This is the recommended pattern for production RAG deployments.
A financial services company is deploying a RAG application that processes customer loan applications using Databricks Model Serving. The compliance team requires that all prompt inputs containing PII must be automatically redacted before being sent to the foundation model, and all model responses must be logged for audit purposes. Which combination of Databricks features BEST meets these governance requirements?
AI Gateway provides built-in PII detection and redaction filters that operate in real-time before requests reach the model, and Model Serving inference tables automatically capture all inputs and outputs to Delta tables for compliance auditing. This is the native Databricks approach for governed GenAI deployments.
A company is evaluating their RAG application's responses for factual accuracy. They want to measure how well the generated answers are grounded in the retrieved context documents. Which MLflow evaluation metric should they use?
faithfulness (also called groundedness) specifically measures whether the generated answer is factually consistent with and supported by the retrieved context. It's the primary metric for verifying that LLM responses don't hallucinate beyond what's in the source documents.
An MLOps team needs to deploy a fine-tuned LLM for real-time inference with guaranteed latency SLAs. The model must serve 500 requests per second during peak hours and scale down during off-peak to control costs. Which Databricks deployment option should they choose?
Databricks Model Serving with provisioned throughput is the only option designed for real-time inference with SLA guarantees. GPU-enabled compute handles LLM inference efficiently, auto-scaling policies handle the 500 RPS peak requirement while scaling down during off-peak (cost optimization), and the service provides latency monitoring and guarantees. This is the production-grade solution for LLM serving.
A company is building a RAG (Retrieval-Augmented Generation) application and needs to chunk 50,000 PDF documents containing technical documentation. Each document averages 30 pages with complex tables, code snippets, and diagrams. The chunks must preserve semantic meaning across page boundaries. What is the MOST efficient approach to implement this chunking pipeline in Databricks?
Using Spark's binary file data source distributes PDF processing across the cluster for parallel execution. PyMuPDF handles complex PDF elements (tables, code) effectively, and pandas UDFs enable semantic chunking logic while maintaining Spark's distributed processing benefits. Sliding windows preserve context across page boundaries, which is critical for RAG quality.
A development team is building a RAG application that retrieves documents from a Vector Search index and passes them to a foundation model. During testing, they notice that retrieved documents sometimes contain sensitive PII that should not be sent to the LLM. The team needs to redact PII before context is passed to the model while maintaining document relevance. Which solution BEST addresses this requirement?
Applying PII detection and redaction as a processing step in the retrieval pipeline (both pre-embedding and post-retrieval) ensures that sensitive information is handled consistently. Using a Python UDF with established PII detection libraries provides flexibility, can be integrated into the RAG workflow, and allows for customization of redaction rules while maintaining the semantic meaning needed for retrieval relevance.
An MLOps engineer needs to implement a CI/CD pipeline for a GenAI application that includes automated testing of prompt templates before deployment. The pipeline must validate that prompt changes don't degrade output quality. Which testing strategy should be implemented?
MLflow's evaluate() function with LLM-as-a-judge is the standard approach for automated prompt testing. It allows you to compare prompt versions against golden test datasets using metrics like faithfulness, relevance, and correctness scored by a judge model. This provides quantitative validation before deployment.
An organization has deployed multiple LLM-powered chatbots across different business units using Databricks Model Serving. The security team needs to enforce that no model can access customer data from regions outside its designated geography due to data residency requirements. What is the MOST effective governance approach?
Unity Catalog metastores can be deployed regionally, and workspace-to-metastore assignments create hard boundaries ensuring models in a workspace can only query data in their assigned regional metastore. This provides the strongest data residency guarantee at the architectural level.
A team needs to monitor their production LLM application for potential toxic or harmful outputs. They want to implement automated monitoring that flags problematic responses in real-time. Which Databricks feature should they configure?
Inference Tables automatically capture all model inputs and outputs in Delta tables, and can be configured with additional monitoring models (like toxicity classifiers) that run in real-time to flag harmful content. This is the purpose-built solution for LLM observability in Databricks.
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How much does the Databricks GenAI Engineer Associate exam cost?
The exam fee is approximately $200 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 6 domains: Design Applications (14%), Data Preparation (14%), Application Development (30%), Assembling and Deploying Applications (22%), Governance (8%), Evaluation and Monitoring (12%). The full course has a dedicated chapter, lab and practice-test coverage for each.
Is this practice test really free?
Yes — all questions on this page are free with explanations and no sign-up. The paid Udemy course adds two full-length timed exams, video lessons and hands-on labs.
How do I get the discount?
Use code FREETEST33 at checkout for $66.99 (list $199.99) through August 23 — the enroll button applies it automatically.
Will this prepare me for the real exam?
The questions mirror the real exam's style and are mapped to the official domains. This is exam-focused preparation — combine the free test with the full course's timed simulations to gauge your readiness.