Databricks Certified Machine Learning Professional Practice Tests

Databricks weights the Machine Learning Professional exam across three domains: Model Development at 44%, MLOps at 44%, and Model Deployment at 12%, per the exam guide covering the version live since September 30, 2025. Model Development spans SparkML pipeline construction, distributed training and hyperparameter tuning with Ray and Optuna, advanced MLflow usage such as nested runs, custom metrics, and custom PyFunc objects, and advanced Feature Store concepts like point-in-time correctness and online tables. MLOps carries equal weight and is the exam's most sprawling domain: model lifecycle architecture, unit and integration testing strategies, Databricks Asset Bundles for environment-as-code, automated retraining triggers, and, its single largest cluster of objectives, Lakehouse Monitoring drift detection across numerical, categorical, prediction, and model-health signals. Model Deployment is the smallest domain but still demands fluency with blue-green and canary rollout strategies and querying custom PyFunc models via REST API or the MLflow Deployments SDK. This is what separates Professional from Associate-level Databricks ML certs: Associate tests whether you can build and track a model, Professional tests whether you can run that model in production at enterprise scale, with monitoring, testing, and safe rollout built in. This 622-question practice bank mirrors that split, weighting Model Development and MLOps evenly and giving Lakehouse Monitoring, roughly nine of the exam's 46 objectives, real depth.

On exam day you get 59 scored multiple-choice questions in 120 minutes, just over two minutes per question, and it is tight because most questions are scenario-based rather than definitional, presenting a real Databricks workflow and asking you to pick the correct API, tool, or configuration. Databricks may also mix in unscored items collected for statistical research, with extra time factored in to cover them, though you will not be told which questions are unscored. The exam is delivered online and proctored, with no test aids permitted. A numeric passing score is not officially published, but community-reported results, including candidates failing in the high-60s and others passing just above the line, confirm the widely cited 70% threshold, which matches this site's own catalog data for the exam. The certification is valid for two years; recertifying means retaking whatever the current live version of the exam is at that time, so content and domain weights can shift between attempts. This page reflects the September 2025 exam guide, the version live as of this writing.

There is no formal prerequisite to register, and Databricks does not require the Machine Learning Associate certification first, but the official exam guide 'highly recommends' course attendance plus one year of hands-on experience with Databricks ML tooling. Databricks lists the instructor-led (or self-paced, via Databricks Academy) courses Machine Learning at Scale and Advanced Machine Learning Operations as its recommended preparation path. The official exam costs USD $200 plus applicable local tax, booked through Databricks' WebAssessor registration portal. Community threads describe the Professional exam as noticeably harder than Associate-level material, with even experienced Databricks users failing on a first attempt, because it leans on production judgment, like test strategy, monitoring configuration, and rollout risk, more than syntax recall. Start with the 30 free questions on this page, then work through the full 622-question bank until your accuracy holds steady across all three domains, weighting Model Development and MLOps practice evenly since together they make up 88% of the exam.

The Databricks Certified Machine Learning Professional (DCMLEP) certification assesses an individual's ability to design, implement, and manage enterprise-scale machine learning solutions using advanced Databricks platform capabilities. Per the exam guide covering the version live since September 30, 2025, it validates proficiency in building scalable ML pipelines with SparkML, implementing distributed training and hyperparameter tuning with Ray and Optuna, leveraging advanced MLflow features such as nested runs and custom PyFunc model objects, and using advanced Feature Store concepts including point-in-time correctness and online tables for low-latency serving.

The certification also evaluates MLOps expertise: unit and integration testing strategies across dev/test/prod stages, environment management with Databricks Asset Bundles (DABs) as infrastructure-as-code, automated retraining workflows triggered by drift or performance degradation, and production monitoring with Lakehouse Monitoring for feature, label, prediction, and model-health drift. Deployment topics cover blue-green and canary rollout strategies, custom PyFunc model registration in Unity Catalog, and querying served models via REST API or the MLflow Deployments SDK. Passing signals the ability to run production-ready ML systems at enterprise scale, not just build and track individual models.

This certification is designed for machine learning engineers, MLOps engineers, and senior data scientists who own production ML systems on Databricks rather than just experimentation. Databricks recommends at least one year of hands-on experience performing the advanced tasks in the exam guide, so it targets practitioners already comfortable with Databricks, SparkML, and MLflow who now need to demonstrate enterprise-scale deployment, testing, and monitoring skills.

Typical candidates hold titles such as Machine Learning Engineer, MLOps Engineer, ML Platform Engineer, or Senior Data Scientist. It also suits those who already hold the Databricks Certified Machine Learning Associate credential and want to prove deeper, production-focused expertise, though Databricks does not require the Associate certification as a formal prerequisite.

There is no formal prerequisite required to register for this exam. The official exam guide states that course attendance and one year of hands-on experience performing the machine learning tasks it covers are 'highly recommended,' but neither is enforced at registration. Candidates are expected to have working knowledge of Python and major ML libraries (scikit-learn, SparkML, MLflow), plus familiarity with Lakehouse Monitoring and Databricks Model Serving.

Databricks recommends the instructor-led courses Machine Learning at Scale and Advanced Machine Learning Operations, both also available self-paced through Databricks Academy, as direct preparation. The Databricks Certified Machine Learning Associate credential is not a required prerequisite, but candidates who hold it typically arrive with the SparkML and MLflow fundamentals this exam builds on.

The exam consists of 59 scored multiple-choice questions to be completed within 120 minutes. All questions are multiple-choice, with no hands-on labs; most are scenario-based, presenting a realistic Databricks ML workflow and asking candidates to choose the correct tool, API, or configuration rather than recall a definition. The exam may include a small number of unscored items collected for statistical research; these are not identified on the exam form and do not affect the final score, and additional time is factored in to accommodate them.

The exam is delivered online and proctored, with no test aids permitted, and costs USD $200 plus applicable local tax, registered through Databricks' WebAssessor platform. Databricks does not publish a numeric passing score, though community-reported results consistently indicate 70%. Certification is valid for two years; recertification requires retaking whatever version of the exam is currently live at that time. The version covered here is the one live as of September 30, 2025.

Earning this certification signals readiness for senior ML engineering roles that own production systems end to end, including Machine Learning Engineer, MLOps Engineer, ML Platform Engineer, and AI/ML Architect titles. General industry data for U.S. machine learning engineers shows base salaries roughly in the $128,000 to $183,000 range, with senior ML engineers often in the $165,000 to $210,000 base range before bonus and equity; a relevant certification is commonly cited as adding a further $5,000 to $15,000 to an offer, though these figures reflect broader ML engineering compensation trends rather than a Databricks-specific salary study. Organizations running the Databricks Lakehouse Platform at production scale, particularly in finance, healthcare, retail, and technology, look for this credential as evidence a candidate can handle MLOps testing, monitoring, and safe deployment, not just model building.