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 data scientist is using MLflow autolog with a PyTorch Lightning training script. The team needs to log custom metrics beyond what autolog captures, while still benefiting from automatic parameter and model logging. Which configuration should they use? (Select one!)
ASet mlflow.autolog exclusive parameter to True and manually log additional metrics
BSet mlflow.autolog exclusive parameter to False and manually log additional metrics
CDisable autolog completely and manually log all parameters and metrics
DUse autolog without any manual logging to avoid conflicts
Explanation
Setting exclusive to False allows manual logging alongside automatic logging, enabling custom metrics while retaining autolog benefits for parameters, models, and standard metrics. Setting exclusive to True would disable manual logging and prevent custom metric recording. Disabling autolog completely eliminates the convenience of automatic parameter and model logging, increasing code complexity. Using autolog without manual logging prevents capturing domain-specific or custom evaluation metrics required by the team.
2. A machine learning team is using TorchDistributor to train a computer vision model on a Databricks cluster with 4 worker nodes, each with 2 GPUs. They want to distribute training across all worker GPUs using NCCL backend for optimal performance. Which configuration should they use? (Select one!)
Anum_processes=8, local_mode=False, use_gpu=True
Bnum_processes=2, local_mode=False, use_gpu=True
Cnum_processes=4, local_mode=False, use_gpu=True
Dnum_processes=8, local_mode=True, use_gpu=True
Explanation
Setting num_processes=8 accounts for all 8 GPUs across the 4 worker nodes (4 nodes × 2 GPUs each). Setting local_mode=False distributes training across worker nodes rather than running subprocesses on the driver. Setting use_gpu=True enables GPU usage and automatically configures the NCCL backend for multi-GPU communication. Using local_mode=True would run all 8 processes on the driver node only, not utilizing worker nodes. Setting num_processes=4 would only use 4 of the 8 available GPUs. Setting num_processes=2 would severely underutilize available GPU resources.
3. A machine learning engineer is optimizing point-in-time feature lookups for a time series feature table containing millions of rows. The feature table has primary keys customer_id, region_id, and event_timestamp. Query performance for training set creation is slow. The team is using databricks-feature-engineering version 0.7.0 with Photon enabled. Which combination of optimizations should they implement to achieve the best performance? (Select two!)
Multiple correct answersAApply Liquid Clustering to the feature table
BReduce primary key columns to customer_id and event_timestamp only
CEnable use_spark_native_join=True when creating the training set
DSet lookback_window to timedelta(days=0) to retrieve only exact timestamp matches
EConvert the feature table to Z-Ordering instead of Liquid Clustering
Explanation
Liquid Clustering is the recommended optimization for databricks-feature-engineering version 0.6.0 and above, providing better performance than Z-Ordering for time series feature tables. Setting use_spark_native_join=True when Photon is enabled leverages Photon's optimized join capabilities for faster point-in-time lookups. While Databricks recommends limiting primary key columns to two for performant writes and lookups, removing region_id may not be acceptable if it's required for the business logic. Setting lookback_window to zero restricts joins to exact timestamp matches only, which may reduce result completeness and doesn't improve join performance. Converting to Z-Ordering when Liquid Clustering is available in version 0.7.0 would be a downgrade since Liquid Clustering provides superior performance for newer versions.
4. An MLOps engineer is monitoring a classification model using Lakehouse Monitoring. The model predicts customer churn using categorical features including region, customer_segment, and subscription_type. Which statistical test will Lakehouse Monitoring use to detect drift in these categorical features? (Select one!)
AChi-squared test
BWasserstein distance
CKolmogorov-Smirnov test
DPopulation Stability Index
Explanation
Lakehouse Monitoring uses the chi-squared test to detect drift in categorical features by comparing frequency distributions between baseline and current data. The chi-squared test evaluates whether observed category frequencies differ significantly from expected frequencies. The Kolmogorov-Smirnov test is used exclusively for numeric features to compare cumulative distribution functions. Wasserstein distance also applies to numeric data, measuring the earth mover distance between distributions. Population Stability Index is used for numeric features to detect distribution shifts, with PSI values above 0.2 indicating significant drift.
5. An MLOps engineer needs to list all active webhooks configured for a specific model using the MLflow Registry Webhooks REST API. They initially wrote code using the POST method to the list endpoint, but it's failing. What should they change? (Select one!)
AChange the HTTP method from POST to GET
BUse PATCH instead of POST
CChange the endpoint URL to /api/2.0/mlflow/webhooks/list
DAdd authentication headers to the POST request
Explanation
The MLflow Registry Webhooks REST API uses GET for listing webhooks, not POST. The correct endpoint is /api/2.0/mlflow/registry-webhooks/list with GET method. POST is used for creating webhooks. Authentication headers are required for all API calls, so adding them wouldn't fix the method error. The endpoint URL /api/2.0/mlflow/registry-webhooks/list is correct, not /api/2.0/mlflow/webhooks/list. PATCH is used for updating existing webhooks, not listing them.
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