What the exam actually feels like

This exam is much more product-specific than people expect. It is not a broad "GenAI theory" test. Databricks wants to know whether you can choose the right Databricks-native component for a real implementation: Vector Search vs plain retrieval, Model Serving vs batch inference, agent evaluation vs generic offline metrics, governance with Unity Catalog, and when to use Mosaic AI capabilities instead of stitching together custom code.

Expect mostly standard multiple-choice and multiple-select questions rather than hands-on labs. The pressure comes from scenario wording, not from calculations. The exam tends to describe a team building a chatbot, internal search assistant, document Q&A workflow, or tool-using agent, then asks for the best Databricks service or workflow step. A lot of wrong answers are plausible in a generic ML sense but are not the best answer in the Databricks ecosystem.

Time-wise, you should think in terms of roughly a little over a minute per question. That matters because some questions are short definition checks, while others are architecture scenarios with 4 answer choices that differ by one service boundary. Do not spend equal time on every item. My approach was:

Answer the direct product-definition questions immediately.
Mark architecture questions that require comparing two Databricks services.
Save the longest RAG/agent design scenarios for a second pass.

If the interface allows review, use it aggressively for multi-select items. Those are where Databricks hides the traps: one option will be technically true, but not aligned with the asked goal such as lowest latency, best governance, production monitoring, or managed Databricks-native approach.

What to prioritize: the topics that show up over and over

If your study time is limited, bias heavily toward the parts of the platform that support production GenAI systems on Databricks, not just model concepts.

The recurring high-value areas are usually these:

RAG architecture on Databricks
- document ingestion
- chunking and embedding flow
- vector indexing and retrieval
- grounding prompts with retrieved context
Mosaic AI / Databricks-native GenAI tooling
- model serving endpoints
- evaluation workflows
- inference patterns
- agent-related capabilities
Vector Search and embeddings
- what is stored where
- when an index is used
- how retrieval fits into a serving flow
Governance and security
- Unity Catalog implications
- access control to data and models
- governed AI assets vs ad hoc pipelines
Monitoring and quality
- evaluating answer quality, groundedness, latency, and production behavior
- understanding why pure accuracy language from classical ML does not fully answer GenAI quality questions

I would spend less time on abstract transformer internals unless your materials explicitly emphasize them. The exam is much more likely to ask, "Which Databricks component should this team use to serve an LLM-backed application with low-latency online inference?" than to ask you to explain positional encodings.

A blunt prioritization order:

Databricks RAG stack end-to-end
Model Serving, endpoints, and deployment choices
Vector Search behavior and retrieval workflow
Evaluation/monitoring of GenAI apps and agents
Governance through Unity Catalog
General LLM concepts only as needed to interpret scenarios

RAG questions: where most people lose points

The exam likes RAG because it lets Databricks test several products in one scenario. You need to be comfortable tracing the full path from raw documents to final answer generation.

The pattern often looks like this: a company has PDFs, wiki pages, tickets, or policy documents; they need a chatbot that answers using internal knowledge; they care about freshness, relevance, and governed access. Then the options mix together ingestion, embeddings, vector search, prompt construction, and serving.

The traps I saw repeatedly:

Confusing embedding generation with vector retrieval.
- Embeddings convert text into vectors.
- Vector Search stores/indexes vectors and retrieves relevant chunks.
- If an answer says Vector Search creates embeddings by itself in every scenario, be careful; the workflow matters.
Ignoring chunking.
- Databricks questions may not ask chunk size directly, but they often imply poor retrieval because documents are too large or too broad.
- If the scenario says answers are vague or irrelevant, retrieval granularity is usually part of the issue.
Picking fine-tuning when the problem is clearly knowledge grounding.
- If the company wants answers based on changing internal documents, the right move is usually RAG, not retraining or fine-tuning a base model.
Missing the governance angle.
- If the prompt mentions sensitive enterprise data, governed access through Databricks data/model tooling matters. The best answer is often the one that keeps the pipeline inside governed Databricks services rather than exporting data externally.

When you get a RAG scenario, mentally separate it into five layers:

Source data: where the documents live
Preparation: parsing, chunking, embedding
Index/retrieval: vector index and similarity search
Generation: model endpoint receives prompt plus retrieved context
Evaluation/monitoring: whether answers are grounded and useful

If an answer choice skips one of those layers, it is often incomplete.

Also watch for wording around freshness. If the business needs near-real-time access to newly added content, prefer answers that imply an updateable ingestion/indexing flow, not a one-time embedding job. Databricks likes to test whether you understand that RAG quality depends not only on the model but on how current and retrievable the knowledge base is.

A practical shortcut: when the question asks how to reduce hallucinations in an enterprise assistant, the correct answer is usually some combination of:

improve retrieval quality
ground the prompt with relevant chunks
evaluate groundedness/faithfulness
use the right Databricks retrieval/serving architecture

It is usually not "choose a larger model" as the primary fix.

Model Serving, endpoints, and deployment choices

This is another area where generic ML instincts can hurt you. Databricks is testing whether you know when to use online serving endpoints versus offline or notebook-driven inference. If the scenario is a user-facing app, chatbot, API, or agent that must respond interactively, think Model Serving first.

Questions here often pivot on operational requirements:

low latency
scalable concurrent inference
production API access
managed deployment
monitoring and lifecycle control

If the use case is batch enrichment of a large dataset, that is a different pattern. But if the question says a support assistant must answer employees in real time, a batch job answer is wrong even if technically possible.

Common traps:

Choosing a training or notebook workflow for a production inference problem.
Confusing model registry/governance with serving.
- Registry and governance help manage assets.
- Serving endpoints expose models for online inference.
Missing that Databricks may prefer a managed endpoint answer over custom infrastructure.

I also saw scenario wording that tests whether you understand the difference between simply calling an LLM and building a governed production application around it. If one answer says, in effect, "host the model with Databricks Model Serving and integrate with the retrieval pipeline," and another says, "have analysts run notebooks that call the model," the first is the exam answer.

You should be able to answer these quickly:

Which Databricks feature supports low-latency online model inference? → Model Serving.
What do you use when an LLM app needs an API endpoint for production use? → Serving endpoint.
What if the requirement is enterprise control and governance around models and data? → Expect Unity Catalog and managed Databricks components to appear in the right answer.

If a question includes cost or performance tradeoffs, do not overthink hidden infrastructure details unless they are explicitly in scope. Databricks usually wants the most appropriate managed architecture, not an exotic optimization.

Vector Search and embeddings: easy on paper, tricky in answer choices

People underestimate this section because "embeddings + vector DB" sounds straightforward. The exam makes it slippery by mixing similar-sounding operations.

Know these distinctions cold:

Embedding model: transforms text/query into vector representations.
Vector index/search: stores vectors and retrieves nearest neighbors.
Reranking / prompt assembly: happens after retrieval, before generation.
Generation model: produces the final answer from the prompt plus context.

The trick is that Databricks will often give four answers that all mention embeddings, but only one matches the right stage in the pipeline.

Examples of gotchas:

If the problem is poor retrieval relevance, changing the generation model may not fix it.
If the problem is stale knowledge, recomputing prompts alone will not fix it; the index/update process matters.
If the question asks how to search semantically similar chunks, keyword search language is usually a distractor unless the answer explicitly frames hybrid retrieval.

I would memorize a simple flow for semantic retrieval questions:

ingest documents
split into chunks
compute embeddings for chunks
store/index vectors
embed the user query
retrieve nearest chunks
pass retrieved chunks to the LLM

If an answer breaks this sequence in a way that makes no architectural sense, eliminate it.

Another pattern: Databricks may ask what improves answer quality in a knowledge assistant. The strongest Databricks-specific answers are often around retrieval quality improvements, evaluation, and managed vector search integration. Generic answers like "increase temperature" or "use a larger context window" are often distractors unless the scenario specifically points there.

Agent and evaluation questions are less obvious than they look

This exam is not just about static prompt-response apps. Expect some coverage of agents, tool use, and how Databricks evaluates GenAI applications. People miss these because they study only RAG setup and model deployment.

The exam tends to distinguish between:

a simple LLM call
a RAG application
an agent that can choose tools/actions

If the scenario includes multi-step reasoning, calling external tools, querying systems, or orchestrating actions beyond retrieval, think agent behavior, not just prompt engineering.

The evaluation part is where generic ML habits fail. Databricks is not only asking whether you know traditional metrics. For GenAI apps, especially RAG and agents, you need to think in terms of:

groundedness / faithfulness to retrieved context
relevance of retrieved chunks
answer quality from an end-user perspective
latency and production behavior
traceability and observability of the application flow

A classic trap is choosing a classical supervised metric answer for a question that is really about LLM application quality. If the scenario says users report plausible but unsupported answers, accuracy/F1 language is usually not enough. The better answer involves evaluating whether outputs are grounded in retrieved evidence.

For agent questions, pay attention to whether the problem is:

selecting the right tool
safely invoking tools
evaluating multi-step outputs
monitoring agent behavior in production

If one option talks only about prompt wording and another talks about agent evaluation or observability, the second is often more aligned with what Databricks wants.

This is also one of the places where product naming matters. If your prep material covered Mosaic AI Agent-related workflows, do not treat them as trivia. Databricks likes testing whether you can map a business need to a named platform capability.

Governance and Unity Catalog: the quiet scoring section

This section does not feel flashy, but it is a reliable source of points if you know how Databricks frames enterprise AI. Whenever the scenario mentions regulated data, internal documents, restricted access, or cross-team reuse, expect governance to matter.

What Databricks wants you to internalize is that GenAI systems are not separate from the lakehouse governance model. Data, models, and AI assets should be handled in a governed way.

Watch for these clues in questions:

"Only authorized teams should access the documents used for retrieval"
"The company needs centralized governance for data and AI assets"
"The model and data must comply with enterprise access controls"

When you see those, answers involving Unity Catalog and managed Databricks controls usually beat DIY external handling.

The trap is that some distractors are technically feasible but violate the spirit of governed enterprise architecture. Example: exporting embeddings or documents into an unmanaged external workflow may work, but if the question emphasizes auditability and centralized permissions, it is not the best answer.

A good way to study this section is to ask of every architecture component: how is access controlled, who can use it, and is it aligned with Databricks governance? That mindset helps on scenario questions far more than memorizing a feature list.

What I would drill in the final week

The last week should be mostly scenario practice, not passive reading. For this exam, flashcards alone are not enough because the hard part is recognizing the correct Databricks component in a realistic workflow.

My final-week checklist would be:

Rebuild a mental map of the Databricks GenAI stack
- data sources
- chunking/embedding
- Vector Search
- model endpoint
- evaluation/monitoring
- governance
Review every place where you confuse two adjacent concepts
- embeddings vs vector index
- serving vs registry/governance
- RAG vs fine-tuning
- simple LLM app vs agent
Practice identifying the primary bottleneck in scenario questions
- hallucination due to missing grounding
- latency due to wrong deployment pattern
- stale answers due to outdated retrieval corpus
- poor enterprise fit due to weak governance
Rehearse product-to-requirement mapping
- real-time inference → Model Serving
- semantic retrieval over enterprise docs → Vector Search + embeddings workflow
- governed enterprise AI assets → Unity Catalog-oriented answer
- answer quality/groundedness checks → GenAI evaluation workflow

If you have access to 86 practice questions on Truekia, use them in a very specific way:

Tag every miss by product area, not just by topic.
Keep a short error log with headings like:
- "picked fine-tuning instead of RAG"
- "mixed up serving and batch inference"
- "forgot governance clue in question stem"
Re-answer only the missed Databricks architecture questions two days later without notes.

Do not spend your final days memorizing obscure LLM science unless your practice results show that as a real weakness. Most candidates lose more points on Databricks workflow mapping than on raw AI theory.

Small interface and exam-day habits that matter for this cert

Because this exam has a lot of scenario-based multiple-choice items, your review strategy should be selective. The questions worth revisiting are not the short definitional ones; they are the ones where two answers both sound valid in general but only one is the most Databricks-native and production-appropriate choice.

On marked questions, use this tie-break order:

Which option is the best fit for a Databricks-managed production implementation?
Which option directly addresses the stated bottleneck: retrieval, serving, evaluation, or governance?
Which option matches the app type: batch, RAG app, or agent?
Which option preserves governed access to enterprise data?

If a multi-select item includes one broad best-practice statement and one concrete Databricks feature statement, do not assume both belong. Databricks sometimes includes a generally true statement that is not necessary for the asked requirement. Choose the options that solve the exact scenario.

The final thing I would say bluntly: this exam punishes vague studying. If your prep notes say things like "learn RAG" or "understand Databricks AI tools," that is not enough. You need to be able to answer in one sentence:

why RAG is better than fine-tuning for changing enterprise knowledge
when Model Serving is the right answer
what Vector Search is doing in the pipeline
how governance changes the architecture choice
what evaluation means for a GenAI app beyond standard ML metrics

If you can do that quickly and consistently, the exam becomes much more manageable.