Databricks Certified Machine Learning Professional Practice Exam
Practice the advanced, production-scale ML engineering this exam demands — scalable SparkML pipelines and distributed tuning, MLOps with Databricks Asset Bundles and Lakehouse Monitoring, and enterprise deployment strategies. Get immediate feedback in Learn mode and a full 120-minute simulation in Exam mode. Start with a 24-hour free trial.
Start 24-hour free trial →Exam at a glance
- Exam
- Databricks Certified Machine Learning Professional
- Format
- Multiple choice, proctored (online or test center)
- Scored questions
- 59 (additional unscored items may appear)
- Time limit
- 120 minutes
- Registration fee
- $200 USD, plus applicable local taxes
- Prerequisites
- None; related training highly recommended
- Recommended experience
- 1+ year of hands-on ML work on Databricks
- Passing standard
- Databricks does not publish a fixed numeric passing score
- Validity
- 2 years; recertify by taking the current exam
- Languages
- English
- Blueprint edition
- ML Professional Exam Guide (Sept 2025 edition)
Source: Databricks — Certified Machine Learning Professional · Exam Guide PDF
About this certification
The Machine Learning Professional is Databricks’ advanced ML credential, aimed at engineers who do not just train models but architect and operate complete ML systems in production at enterprise scale. It assumes the fundamentals covered by the Associate and goes deeper: building scalable SparkML pipelines, running distributed training and hyperparameter tuning, using advanced MLflow features, and building automated feature pipelines with the Feature Store. The defining theme is MLOps — testing strategies, environment management with Databricks Asset Bundles, automated retraining workflows, and drift detection with Lakehouse Monitoring.
The exam is heavily applied and scenario-based: questions describe a realistic production situation — configuring a monitor for drift, designing a retraining trigger, choosing a deployment strategy — and ask for the most effective Databricks approach. It also assesses SQL ability, adhering to ANSI SQL standards. Because the structure changed from the older four-section blueprint, candidates returning after a gap should note the current three-domain layout. For foundational reading on operationalizing ML on Databricks, see the Machine Learning Learning Hub guide.
Exam domains and weights
The current exam is divided into three domains. Weights are taken directly from the official Databricks exam page; approximate question counts are derived from the 59 scored questions and rounded.
Advanced, scalable model building: SparkML pipelines and when to choose them over single-node frameworks, distributed training and tuning (including pandas Function APIs), advanced MLflow tracking, and automated feature pipelines with the Feature Store.
Production operations: testing strategies for ML pipelines, environment management with Databricks Asset Bundles, automated retraining workflows, and drift detection with Lakehouse Monitoring.
Enterprise deployment: custom model serving, deployment strategies such as blue-green and canary rollouts, and managing model rollout across batch, streaming, and real-time scenarios.
Who this exam is for
This credential fits experienced ML engineers, MLOps engineers, and senior data scientists who already operate production ML on Databricks and want to validate enterprise-scale, production-grade expertise. There are no formal prerequisites, so anyone can register; in practice Databricks recommends at least a year of hands-on experience with the tasks in the exam guide. Solid SparkML, MLflow, and Feature Store experience, comfort with ANSI SQL, and real exposure to monitoring and retraining systems are effectively expected.
If you are still building the fundamentals — AutoML, basic MLflow, single-node modeling — start with the Machine Learning Associate first; this Professional exam builds directly on that foundation and is meaningfully harder. For role-by-role salary ranges and career paths, see the Career Hub — Machine Learning Engineer role guide.
What this practice exam delivers
Learn mode
Answer one question at a time with the explanation revealed immediately — ideal for the MLOps material, where the right monitoring, retraining, or asset-bundle decision is rarely obvious on first read.
Exam mode
59 questions against a 120-minute timer — the real exam format. Build the pacing and stamina the longer, scenario-heavy Professional exam requires.
Source-linked explanations
Every answer cites the Databricks documentation it derives from — SparkML, MLflow, Lakehouse Monitoring, Model Serving, Asset Bundles — so you can verify the reasoning and dig deeper.
Score by exam domain
Results break down across all three domains — Model Development, ML Ops, and Model Deployment — so practice tells you exactly where the production gaps are.
Sample practice questions
Ten free questions spanning the three exam domains, each with a full explanation of why the other answers are wrong. The complete bank is available with the 24-hour trial.
A dataset is far larger than a single machine's memory, and training must scale across a cluster. Which approach is most appropriate?
- Collect the data to the driver and train with single-node scikit-learn
- Use SparkML's distributed estimators so training runs across the cluster's workers
- Down-sample to fit one machine and train locally
- Train in a SQL warehouse
Show answer & explanation
Correct: B. SparkML's estimators distribute training across the cluster, which is the right choice when data exceeds single-node memory — a core Model Development skill is choosing SparkML over single-node frameworks based on data size.
Why not the others: collecting to the driver (A) defeats distribution and will run out of memory; down-sampling (C) discards data and changes the problem; a SQL warehouse (D) is for analytics queries, not distributed model training. Match the framework to the data scale.
Source: Databricks — Apache Spark MLlib →An engineer needs to parallelize hyperparameter tuning of a single-node scikit-learn model across a Spark cluster. Which tool is designed for this?
- A plain Python for-loop on the driver
- Hyperopt with SparkTrials to distribute trials across the cluster
- Increasing the driver node size only
- Manually editing parameters between runs
Show answer & explanation
Correct: B. Hyperopt with SparkTrials distributes hyperparameter search trials across Spark workers, parallelizing tuning of single-node models — the intended pattern for scaling tuning on Databricks.
Why not the others: a driver-side loop (A) runs trials serially on one node; a bigger driver (C) adds resources but no parallelism across the cluster; manual edits (D) are not a search strategy. The exam expects distributed, informed tuning.
Source: Databricks — hyperparameter tuning → Further reading: PowerKram — Distributed Hyperparameter Tuning →A feature pipeline must guarantee that training uses only feature values that were available at each label's timestamp, with no leakage from the future. Which Feature Store capability addresses this?
- A standard join on the latest feature values
- A point-in-time lookup / time-series feature join
- Caching features in a temporary view
- Dropping the timestamp column
Show answer & explanation
Correct: B. Point-in-time (time-series) feature lookups join each training row to the feature values as of its event timestamp, enforcing point-in-time correctness and preventing leakage — a key advanced Feature Store concept.
Why not the others: a latest-value join (A) leaks future information into training; a temporary view (C) does nothing for temporal correctness; dropping the timestamp (D) removes the very information needed to do the lookup correctly.
Source: Databricks — point-in-time feature lookups →A team wants to define, version, and deploy their ML jobs, models, and infrastructure as code across dev, staging, and prod. Which Databricks capability is designed for this?
- Copy-pasting notebooks between workspaces
- Databricks Asset Bundles (DABs)
- Exporting a .dbc archive manually
- A single shared notebook for all environments
Show answer & explanation
Correct: B. Databricks Asset Bundles package jobs, models, pipelines, and their configuration as versioned infrastructure-as-code that can be deployed consistently across environments — the MLOps mechanism the exam emphasizes.
Why not the others: copy-pasting notebooks (A) and manual .dbc exports (C) are error-prone and unversioned; one shared notebook (D) eliminates environment separation entirely. DABs provide reproducible, promotable deployments.
Source: Databricks — Asset Bundles → Further reading: PowerKram — Databricks Asset Bundles for MLOps →A production model's input feature distribution has shifted over time. Which Databricks capability detects this drift and alerts the team?
- Lakehouse Monitoring with a monitor on the inference table
- A one-time summary statistics notebook run at launch
- Increasing the model serving endpoint size
- Re-reading the training data once a quarter by hand
Show answer & explanation
Correct: A. Lakehouse Monitoring profiles tables (including inference tables) over time, computes drift metrics with statistical tests, and can drive alerts — the intended tool for detecting data and prediction drift in production.
Why not the others: a one-time notebook (B) cannot track ongoing drift; a bigger endpoint (C) addresses throughput, not data quality; manual quarterly review (D) is neither timely nor automated. Continuous monitoring is the MLOps requirement.
Source: Databricks — Lakehouse Monitoring →A drift monitor has flagged degraded performance and the team wants the pipeline to retrain automatically when this happens. Which approach fits an automated retraining workflow?
- Wait for a user to notice and retrain manually
- Trigger a retraining Job when monitoring metrics breach a defined threshold, then validate and register the new model
- Always serve the original model regardless of drift
- Delete the monitor to stop the alerts
Show answer & explanation
Correct: B. An automated retraining workflow watches monitoring metrics and, when a threshold is breached, triggers a retraining Job whose output is validated and registered before promotion — closing the MLOps loop without manual intervention.
Why not the others: waiting for a human (A) is the gap automation removes; serving the stale model (C) ignores the degradation; deleting the monitor (D) hides the problem rather than fixing it. Threshold-driven retraining is the pattern.
Source: Databricks — MLOps workflows → Further reading: PowerKram — Automated Retraining Pipelines →In the Unity Catalog model registry, what is the recommended way to designate which model version is currently the production model?
- Encode "prod" in the registered model's name
- Assign a model alias (for example, "champion" or "prod") to the chosen version
- Keep only one version by deleting all others
- Store the version number in a separate spreadsheet
Show answer & explanation
Correct: B. In the Unity Catalog model registry, aliases point a stable label (such as a production or champion alias) at a specific model version, so downstream jobs reference the alias while you promote new versions behind it.
Why not the others: naming conventions (A) are cosmetic and fragile; deleting other versions (C) destroys rollback history; an external spreadsheet (D) breaks governance and lineage. Aliases are the supported mechanism in the current registry.
Source: Databricks — manage model lifecycle (aliases) →A team wants to release a new model version to a small percentage of live traffic first, then shift more traffic if metrics look healthy. Which deployment strategy is this?
- A full cut-over replacing the old model instantly for everyone
- A canary deployment that routes a small traffic share to the new version before ramping up
- Batch scoring overnight
- Deleting the old endpoint before testing the new one
Show answer & explanation
Correct: B. A canary deployment exposes the new version to a small slice of live traffic, monitors it, and gradually increases the share — limiting blast radius, which is exactly the rollout-management skill tested in Model Deployment.
Why not the others: an instant full cut-over (A) is the high-risk approach canary avoids; overnight batch scoring (C) is not a live-rollout strategy; deleting the old endpoint first (D) removes the safe fallback. Gradual, monitored rollout is the goal.
Source: Databricks — Model Serving → Further reading: PowerKram — Blue-Green & Canary Deployments →An application needs synchronous, low-latency predictions from a registered model over HTTP. Which Databricks capability serves this?
- A scheduled batch inference Job
- A Model Serving endpoint exposing the model as a REST API
- A static Delta table of precomputed predictions
- A SQL dashboard
Show answer & explanation
Correct: B. Model Serving deploys a registered model behind a scalable REST endpoint for real-time, low-latency inference — the right fit for synchronous per-request predictions.
Why not the others: a batch Job (A) and a precomputed table (C) cannot answer arbitrary live requests with low latency; a SQL dashboard (D) visualizes data and does not serve model predictions. Real-time needs an endpoint.
Source: Databricks — Model Serving (real-time) → Further reading: PowerKram — Real-Time Model Serving →An engineer wants to apply a custom Python model with arbitrary pre- and post-processing logic and have MLflow package it in a standard, deployable format. Which MLflow concept fits?
- Logging only the raw model weights as an artifact
- An MLflow
pyfuncmodel that wraps the custom logic behind a standard predict interface - Saving the code as a text file in DBFS
- Emailing the notebook to the deployment team
Show answer & explanation
Correct: B. An MLflow pyfunc model wraps custom logic — including pre- and post-processing — behind a uniform predict interface, so it can be logged, registered, and served like any other MLflow model.
Why not the others: raw weights alone (A) lose the surrounding logic and packaging; a text file in DBFS (C) is not a deployable model format; emailing a notebook (D) bypasses the registry and serving entirely. pyfunc is the standard custom-model wrapper.
Keep going: Learning & Career resources
This advanced credential pays off fastest when it sits on top of real production experience and a clear sense of where the role leads. Two PowerKram hubs back this exam up.
Deep dive: exam structure, scoring, study path & recertification
Exam structure and how it’s scored
The current exam delivers 59 scored multiple-choice questions in 120 minutes; additional unscored items may appear for calibration, with extra time factored in. Databricks does not publish a fixed numeric passing score on the official exam page, and your result is reported as pass or fail. Questions are applied and scenario-based, and the exam also assesses SQL ability adhering to ANSI standards, so production experience matters as much as theory. Read the exam-format deep dive →
What the three domains actually test, and what changed
The current blueprint is three domains: Model Development (44%), ML Ops (44%), and Model Deployment (12%). This replaced an older four-section layout (Experimentation, Model Lifecycle Management, Model Deployment, Solution and Data Monitoring), so older study notes can mislead. The two big domains carry equal weight, which means MLOps — Asset Bundles, automated retraining, Lakehouse Monitoring, pipeline testing — is just as important as model building. Underestimating MLOps is the most common preparation mistake. Read the Databricks production-ML toolchain guide →
Realistic study path
Plan roughly eight to sixteen weeks depending on production experience. A workable path: the Databricks Academy advanced courses (machine learning at scale and advanced ML operations), then build an end-to-end production project — a SparkML pipeline with distributed tuning, an automated feature pipeline with point-in-time correctness, a deployment via Model Serving with a canary rollout, and a Lakehouse Monitoring monitor wired to an automated retraining Job. Practice the gotchas: point-in-time feature joins, monitor configuration per table type, and Asset Bundle structure. Read the study plan →
Cost, scheduling, and delivery
The registration fee is $200 USD plus applicable local taxes. The exam is proctored and can be taken online or at a test center, and is offered in English. Online delivery requires a quiet private space and a system check through the proctoring provider. Databricks periodically offers discount vouchers through learning events. Verify current fees and scheduling on Databricks’ official page before booking. Databricks’ official certification page →
Recertification
The certification is valid for two years. To stay certified you retake and pass the current version of the exam before it expires — there is no continuing-education-credit alternative. Because Databricks refreshes this exam frequently (the move to Asset Bundles, distributed tuning, and Lakehouse Monitoring is recent), recertifying also keeps your validated skills aligned with current platform practice. Read the recertification guide →
Career outlook
Production ML and MLOps skills are among the most in-demand in the data and AI market, and an advanced, platform-specific credential signals that you can operate ML systems at enterprise scale rather than only build models. The Professional credential is most valuable paired with demonstrable production work — a monitored, automatically retrained, safely deployed pipeline. For salary ranges and role-specific paths, see the Career Hub. Career Hub — Machine Learning Engineer →
Frequently asked questions
Is the Databricks Machine Learning Professional exam hard?
Yes — it is Databricks’ advanced ML certification and is meaningfully harder than the Associate. The 59 questions are applied and scenario-based, and roughly 56% of the weight sits in MLOps and deployment rather than model building, so candidates who focus only on training models tend to come up short. Real production experience with monitoring, retraining, and deployment is what carries it.
What is the passing score?
Databricks does not publish a fixed numeric passing score on the official exam page; results are reported as pass or fail based on overall performance. A 70% figure circulates on third-party sites, but it is not confirmed by Databricks, so treat it as unofficial and aim to be solid across all three domains rather than targeting a specific percentage.
What are the current exam domains? I’ve seen different lists.
The current 2026 blueprint has three domains: Model Development (44%), ML Ops (44%), and Model Deployment (12%). An older version of the exam used four sections — Experimentation, Model Lifecycle Management, Model Deployment, and Solution and Data Monitoring — which is why you may see conflicting lists online. Study against the current three-domain structure on the official exam guide.
Do I need the Associate certification first?
It is not a formal prerequisite — there are no required certifications to register. But the Professional builds directly on Associate-level fundamentals (AutoML, basic MLflow, single-node modeling) and assumes them, so most candidates take the Associate first or have equivalent hands-on experience. Databricks recommends at least a year of relevant production ML work before attempting the Professional.
Which tools and topics should I focus on most?
Because Model Development and ML Ops are weighted equally at 44% each, split your time between scalable SparkML pipelines and distributed tuning on one side, and the MLOps stack on the other — Databricks Asset Bundles, automated retraining, pipeline testing, and Lakehouse Monitoring for drift. Add advanced MLflow, point-in-time Feature Store joins, and deployment strategies (canary and blue-green), and brush up on ANSI SQL.
Is the certification worth it?
For experienced ML and MLOps engineers working on Databricks, it is a strong, advanced signal that you can run production ML at enterprise scale — a more differentiated credential than the Associate. Its value is highest paired with demonstrable production work. At a $200 fee, the main cost is preparation time, and it is valid for two years before you recertify.
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