Exam Logistics and Platform Fundamentals — Databricks Generative AI Engineer practice questions

Which background is the MOST relevant recommended experience for this exam?

1. six or more months of hands-on work building LLM applications on Databricks
2. formal academic training in transformer architecture research
3. holding the Databricks Data Engineer Professional certification
4. experience administering cloud networking, VPC peering and firewall setups

Answer: A — six or more months of hands-on work building LLM applications on Databricks

A) Correct — the guide targets practitioners with roughly six months of applied GenAI work on the platform. B) The exam tests application building, not model research. C) No other certification is a prerequisite. D) Networking administration is out of scope.

Which Delta Lake property is MOST directly useful when a RAG source table is being updated while an index sync runs?

1. ACID transactions with snapshot isolation for readers
2. automatic embedding generation performed on every write
3. columnar compression applied to the document text fields
4. built-in semantic search across the table's string columns

Answer: A — ACID transactions with snapshot isolation for readers

A) Correct — snapshot isolation means the sync reads a consistent version even while writes land. B) Delta does not generate embeddings; that is the vector search or a pipeline's job. C) Compression saves storage but does not address concurrency. D) Semantic search comes from a vector index, not from the table format.

Which statement about the format of the Databricks Certified Generative AI Engineer Associate exam is accurate?

1. it has 45 scored items within a 90-minute window and no lab component
2. it is a four-hour hands-on practical lab exam in a live workspace
3. it is a set of 100 items answered over an unlimited amount of time
4. it requires submitting a portfolio project for review by an expert panel

Answer: A — it has 45 scored items within a 90-minute window and no lab component

A) Correct — 45 scored items in 90 minutes, online proctored, with unscored pretest items possible. B) There is no lab component on this exam. C) Both the item count and the timing are wrong. D) No portfolio submission exists for this certification.

A candidate asks what score they must achieve to pass. The accurate answer is that

1. a fixed 70 percent is required on every Databricks associate exam
2. Databricks does not publish a passing score for this certification
3. a candidate must pass each domain of the exam independently
4. the score is curved against the other candidates taking it that month

Answer: B — Databricks does not publish a passing score for this certification

B) Correct — no cut score is published, so any specific figure circulating is unofficial. A) States an unpublished number as fact. C) There is no per-domain pass requirement; scoring is overall. D) No published curve or cohort-relative scoring exists.

Which domain carries the LARGEST weight on the current exam guide?

1. Governance — securing and controlling GenAI assets
2. Data Preparation — chunking and embedding source data
3. Application Development — building the agent itself
4. Evaluation and Monitoring — judging and tracking quality

Answer: C — Application Development — building the agent itself

C) Correct — Application Development is the heaviest domain at 30 percent. A) Governance is the smallest at 8 percent. B) Data Preparation is 14 percent. D) Evaluation and Monitoring is 12 percent.

Mosaic AI?

Databricks' AI platform: hosts foundation models (DBRX, Llama, Mixtral), enables fine-tuning, deploys via Model Serving, integrates with Unity Catalog + MLflow.

List the six exam domains and their weights.

Design Applications 14% • Data Preparation 14% • Application Development 30% • Assembling and Deploying Applications 22% • Governance 8% • Evaluation and Monitoring 12%.

Prerequisites?

None mandatory. Recommended: 6+ months Databricks + LLM application development experience.

Databricks Lakehouse?

Unified architecture combining data lake (cheap storage) + data warehouse (ACID, fast query) on open formats (Delta Lake). Foundation for ML/GenAI.