Databricks · DCMLEP
Databricks Certified Machine Learning Professional Practice Tests
Validates advanced expertise in designing and managing enterprise-scale machine learning solutions on Databricks, covering scalable model development with distributed training, MLOps practices including testing and deployment with Databricks Asset Bundles, and model monitoring with Lakehouse Monitoring.
Exam Details
Practice Questions
622
≈ 13 practice exams
Duration
120 minutes
Passing Score
70%
Difficulty
ProfessionalLast Updated
Feb 2026
Databricks Certified Machine Learning Professional Practice Exam Preparation
Use this DCMLEP practice exam to prepare for Databricks Certified Machine Learning Professional with realistic questions, detailed explanations, and focused study modes. The practice bank includes 622 questions for Databricks DCMLEP, so you can review the exam steadily instead of relying on one long cram session.
As you practice, pay extra attention to patterns in your missed answers. Start with short sessions to identify weak areas, then move into timed quizzes once your accuracy is consistent.
The explanations are especially useful when you want to connect exam wording to the responsibilities and scenarios described in the official certification guidance. Use the free preview first, then unlock the full question bank when you are ready to build a complete study routine.
Exam Domain Breakdown
Model Development47%
MLOps43%
Model Deployment10%
Exam Overview
The Databricks Certified Machine Learning Professional certification validates advanced expertise in designing, implementing, and managing enterprise-scale machine learning solutions on the Databricks Lakehouse Platform. It covers the full spectrum of production ML engineering: building scalable pipelines with SparkML, implementing distributed training and hyperparameter tuning using Ray and Optuna, and leveraging advanced MLflow capabilities such as nested runs, custom metrics, model flavors, PyFunc custom models, and Model Registry webhooks.
The certification also emphasizes modern MLOps practices, including ML pipeline testing strategies (unit and integration tests), environment management via Databricks Asset Bundles (DABs) for infrastructure-as-code, automated retraining workflows, and production monitoring with Lakehouse Monitoring for detecting feature drift, label drift, prediction drift, and concept drift. Model deployment topics include blue-green and canary deployment strategies, custom model serving endpoints, and rollout management through Databricks Model Serving and the MLflow Deployments SDK. The exam was updated in September 2025 to consolidate its structure into three core domains.
Who Should Take This Exam
This certification is designed for senior ML engineers, MLOps engineers, and data scientists with at least one year of hands-on experience building and operationalizing machine learning workflows on Databricks. It is appropriate for professionals who work at enterprise scale—managing multi-environment ML deployments, automated retraining pipelines, and production monitoring—rather than those focused solely on model experimentation.
Typical candidates hold roles such as Machine Learning Engineer, MLOps Engineer, Senior Data Scientist, or ML Platform Engineer. Those already holding the Databricks Certified Machine Learning Associate credential who want to demonstrate deeper production-level expertise are also a natural fit for this exam.
Prerequisites
There are no formal prerequisites required to register for this exam. However, Databricks strongly recommends at least one year of hands-on experience performing the advanced ML engineering tasks outlined in the official exam guide. Candidates are expected to have practical familiarity with the Databricks platform, Apache Spark and SparkML, MLflow experiment tracking and model registry, and Python-based ML workflows.
Databricks recommends completing the instructor-led courses 'Machine Learning at Scale' and 'Advanced MLOps on Databricks' before attempting the exam. Candidates who hold the Databricks Certified Machine Learning Associate credential will find that foundational knowledge helpful, though it is not a required prerequisite.
Exam Format
The exam consists of 59 scored multiple-choice questions to be completed within 120 minutes. All questions are multiple-choice; there are no hands-on labs or interactive coding tasks. Many questions are scenario-based, presenting real-world Databricks ML workflows and asking candidates to select the most appropriate approach. The exam may also include a small number of unscored items collected for statistical research purposes; these are not identified on the exam form and do not affect the final score, with additional time factored in to accommodate them.
The exam is delivered online through Databricks' exam delivery platform and costs USD $200. A passing score of 70% is required. Certification is valid for two years, after which recertification requires retaking the current version of the exam. The current version of the exam is the September 2025 edition.
Skills Measured
- 1.Domain 1 — Model Development (~47%, 22 objectives): Covers building scalable ML pipelines using SparkML estimators, transformers, and pipeline APIs; distributed training with pandas Function APIs and UDFs; hyperparameter tuning at scale using Ray Tune and Optuna; advanced MLflow usage including nested runs, custom metrics, experiment tracking, and model flavors; and Feature Store implementation including automated feature pipelines and point-in-time lookups.
- 2.Domain 2 — MLOps (~43%, 20 objectives): Covers MLOps architecture patterns and multi-environment setups; ML pipeline testing including unit and integration test strategies; Databricks Asset Bundles (DABs) for managing ML assets as infrastructure-as-code; automated retraining workflows with triggers and orchestration; and production monitoring with Lakehouse Monitoring, including configuring drift detection for feature, label, prediction, and concept drift, and setting up alerting on metric degradation.
- 3.Domain 3 — Model Deployment (~10%, 5 objectives): Covers deployment strategies including blue-green and canary rollouts; deploying models via Databricks Model Serving endpoints; building custom model serving solutions with MLflow PyFunc; managing model versions and traffic splitting; and using the REST API and MLflow Deployments SDK for programmatic deployment management.
Study Tips
- Download and study the official Databricks Certified Machine Learning Professional Exam Guide (available at files.training.databricks.com) to understand the exact objectives and their distribution across the three domains before building your study plan.
- Prioritize Lakehouse Monitoring deeply — it appears in approximately 10 exam objectives. Understand all four drift types (feature, label, prediction, concept), the statistical tests used to detect each, and how to configure monitor profiles and alerting in the Databricks UI and via the API.
- Complete the official Databricks instructor-led courses 'Machine Learning at Scale' and 'Advanced MLOps on Databricks', then work through the end-to-end MLOps demo notebooks in the Databricks documentation to see how Asset Bundles, model serving, and monitoring connect in a production workflow.
- Practice with Databricks Asset Bundles (DABs) hands-on using the Databricks Free Edition. Know how to define ML assets (jobs, model serving endpoints, MLflow experiments) in YAML bundle configuration files and deploy them across dev/staging/prod environments.
- Master MLflow's advanced features beyond basic experiment tracking: nested runs for hyperparameter search logging, custom PyFunc model classes, model signatures and input examples, Model Registry stage transitions, and webhook configurations for CI/CD triggers.
- Build fluency with distributed hyperparameter tuning: understand when to use Ray Tune vs. Optuna, how to integrate them with MLflow for run logging, and how SparkML's CrossValidator and TrainValidationSplit compare to these libraries for tuning at scale.
- Use scenario-based practice questions that mirror real Databricks workflows — the exam is heavily applied. Focus on recognizing the correct tool or API for a given production situation rather than memorizing definitions.
Career Benefits
Earning this certification positions ML engineers and data scientists for senior and staff-level roles that require end-to-end ownership of production ML systems. Job titles commonly associated with this credential include Senior Machine Learning Engineer, MLOps Engineer, ML Platform Engineer, and AI/ML Architect. Organizations adopting the Databricks Lakehouse Platform at scale—particularly in finance, healthcare, retail, and technology sectors—actively seek professionals who can demonstrate validated expertise in production ML workflows rather than just model development.
Sample Questions
5 sample questions with answers and explanations. The full bank has 622 questions, enough for 13 full-length practice exams.
Preview — answers shown1. A machine learning team is migrating ML workflows from development to production using Databricks Asset Bundles. The development environment uses catalog dev_ml and runs as the data scientist's user account, while production uses catalog prod_ml and must run as a service principal named prod-ml-sp. How should they configure the databricks.yml targets section? (Select one!)
ASet mode to development for dev target and production for prod target, with run_as configured only in prod
BUse mode development for both targets and configure catalog variables only
CSet mode to production for both targets and configure different service principals
DSet run_as to service principal for both dev and prod targets
Explanation
Databricks Asset Bundles require mode set to development for dev environments (runs as the deploying user) and production for prod environments. The run_as configuration with service_principal_name should only be set in the prod target to ensure production jobs run with appropriate service principal permissions, while development runs as the individual developer for easier debugging. Setting mode to production for both environments would apply production restrictions to development. Configuring service principals for dev targets adds unnecessary complexity and restricts development flexibility. Using mode development for production environments bypasses important production safeguards and permissions isolation.
2. An MLOps engineer is analyzing model serving endpoint logs stored in an inference table. They need to join inference predictions with ground truth labels that arrive 24-48 hours later. Which column should they use as the join key to correlate predictions with labels? (Select one!)
Atimestamp_ms which captures the inference time
Brequest which contains the raw JSON payload
Cdatabricks_request_id which is automatically generated
Dclient_request_id which is user-provided for correlation
Explanation
The client_request_id column is specifically designed for correlating predictions with ground truth labels. Users provide this ID in the inference request, and it can be used later to join predictions with corresponding labels when they become available. The databricks_request_id is Databricks-generated and not accessible to external systems providing ground truth. The timestamp_ms cannot reliably join predictions with labels due to timing variations. The request column contains raw JSON and is not suitable as a join key. This pattern is essential for monitoring model performance with delayed ground truth feedback.
3. A machine learning team notices that their inference table stopped receiving new logs despite the model serving endpoint processing requests successfully. They check the table and find it contains 520,000 files. What should they do to resume logging? (Select one!)
ACreate a new inference table and update the endpoint configuration
BRun OPTIMIZE on the inference table to compact files and reduce file count
CWait for automatic compaction which runs every 24 hours
DIncrease the sampling_fraction parameter to 1.0 in the endpoint configuration
Explanation
Inference tables stop logging when they exceed 500,000 files, and running OPTIMIZE consolidates small files into larger ones, reducing the file count below the threshold and resuming logging. This is a documented limitation requiring manual intervention through table optimization. The sampling_fraction controls what percentage of requests are logged but does not address the file count limit. Creating a new table would work but loses historical data and is more disruptive than optimizing the existing table. There is no automatic compaction mechanism for inference tables.
4. An MLOps engineer is querying inference tables to analyze prediction performance for a deployed classification model. The inference table captures request and response data with client_request_id for joining with ground truth labels. Which columns are automatically populated in Databricks inference tables? (Select three!)
Multiple correct answersAdatabricks_request_id containing the Databricks-generated unique identifier
Btimestamp_ms containing the request time in epoch milliseconds
Cexecution_time_ms containing the model inference duration
Dmodel_version containing the serving endpoint version number
Eclient_request_id containing the user-provided identifier
Fprediction_confidence containing the model confidence score
Explanation
Databricks Model Serving automatically populates inference tables with databricks_request_id as a system-generated unique identifier, timestamp_ms containing the request timestamp in epoch milliseconds, and execution_time_ms measuring how long the model took to process the request. The client_request_id is user-provided, not automatically generated by the system. The model_version column is not automatically captured in inference tables. The prediction_confidence column is not a standard inference table column as confidence scores vary by model type and are captured within the response JSON field.
5. An ML engineer is implementing a canary deployment for a model serving endpoint. The current champion model serves 100 percent of traffic. They want to gradually roll out a challenger model by first sending 10 percent of traffic, then 25 percent, then 50 percent, and finally 100 percent if performance metrics remain stable. Which Model Serving configuration section controls traffic distribution between model versions? (Select one!)
Adeployment_config with canary_rules specifying version and traffic_ratio
Bload_balancer_config with targets specifying model_alias and weight
Crouting_config with endpoints specifying model_version and percentage_split
Dtraffic_config with routes specifying served_model_name and traffic_percentage
Explanation
The traffic_config section in Model Serving endpoint configuration contains a routes array where each route specifies a served_model_name and traffic_percentage to control traffic distribution. This enables canary deployments by gradually shifting traffic percentages from the champion to challenger model. The routing_config parameter does not exist in Databricks Model Serving configuration. The load_balancer_config parameter is not used for model serving traffic distribution. The deployment_config with canary_rules is not the correct configuration structure for Model Serving endpoints.
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