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Exam Review

Key Facts: Databricks ML Professional Exam

59

Scored Questions

Databricks official exam page

120 min

Exam Duration

Databricks official exam page

$200

Registration Fee

Databricks certification pricing

~70%

Passing Score

Widely reported benchmark

2 years

Validity Period

Databricks recertification policy

3

Exam Domains

Model Development 44%, MLOps 44%, Deployment 12%

Databricks Certified Machine Learning Professional is a 120-minute proctored exam with 59 scored multiple-choice questions and a $200 registration fee. The exam covers Model Development (44%), MLOps (44%), and Model Deployment (12%). Key topics include SparkML pipelines, MLflow (tracking, registry, autologging), Feature Store, Databricks Asset Bundles for CI/CD, Lakehouse Monitoring for drift detection, and Model Serving endpoints. No prerequisites, but 1+ years of hands-on Databricks ML experience is recommended. Validity is 2 years.

Sample Databricks ML Professional Practice Questions

Try these sample questions to review concepts for the Databricks ML Professional exam. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1In MLflow, which component is used to manage the lifecycle of a machine learning model across stages such as Staging, Production, and Archived?
A.MLflow Tracking
B.MLflow Model Registry
C.MLflow Projects
D.MLflow Recipes
Explanation: The MLflow Model Registry provides a centralized model store for managing the full lifecycle of ML models. It supports version tracking, stage transitions (Staging, Production, Archived), annotations, and approval workflows for promoting models.
2When using Spark MLlib for distributed training, which class is used to build a sequence of data transformations and model training steps?
A.SparkSession
B.Pipeline
C.DataFrame
D.Estimator
Explanation: A Spark ML Pipeline chains multiple Transformers and Estimators into a single workflow. It encapsulates the entire data preparation and model training process, ensuring consistent transformations during both training and inference.
3In Databricks, what is the primary purpose of the Feature Store?
A.To store raw data files in cloud object storage
B.To centralize the management, discovery, and serving of feature tables for training and inference
C.To execute SQL queries against Delta Lake tables
D.To schedule and monitor recurring data pipelines
Explanation: The Databricks Feature Store provides a centralized repository for managing ML features. It enables feature discovery, lineage tracking, and consistent feature serving across training and online inference, reducing training-serving skew.
4Which MLflow function logs a trained model along with its signature and input example for reproducibility?
A.mlflow.log_metric()
B.mlflow.sklearn.log_model()
C.mlflow.log_param()
D.mlflow.log_artifact()
Explanation: mlflow.<flavor>.log_model() (e.g., mlflow.sklearn.log_model()) logs a trained model with its flavor-specific metadata, input/output signature, and optional input example. The signature ensures that inference requests are validated against expected data types.
5When performing hyperparameter tuning in Databricks, which library integrates with SparkML to parallelize trial execution across a cluster?
A.Pandas
B.Hyperopt with SparkTrials
C.Matplotlib
D.SciPy
Explanation: Hyperopt with SparkTrials distributes hyperparameter search trials across Spark workers, enabling parallel evaluation of different parameter combinations. This significantly reduces tuning time compared to sequential search on a single node.
6In the context of MLOps on Databricks, what is the purpose of Databricks Asset Bundles (DABs)?
A.To compress model artifacts for storage efficiency
B.To define, validate, and deploy Databricks resources (jobs, pipelines, ML models) as code with CI/CD integration
C.To create data visualizations in notebooks
D.To manage cluster autoscaling policies
Explanation: Databricks Asset Bundles (DABs) enable infrastructure-as-code for Databricks resources. They define jobs, pipelines, and ML model deployments in YAML configuration files that can be version-controlled, validated, and deployed through CI/CD pipelines.
7Which Databricks feature detects data drift by comparing statistical distributions of incoming data against a reference dataset?
A.Unity Catalog
B.Lakehouse Monitoring
C.Delta Live Tables
D.Databricks SQL
Explanation: Lakehouse Monitoring tracks data quality and drift over time by comparing statistical profiles of incoming data against a baseline or reference dataset. It can detect distribution shifts that may degrade model performance, triggering alerts or retraining.
8What is the primary advantage of using Delta Lake tables for ML feature storage compared to standard Parquet files?
A.Delta Lake uses a different file format than Parquet
B.Delta Lake provides ACID transactions, time travel, and schema enforcement for reliable feature data
C.Delta Lake can only be used with Python, not SQL
D.Delta Lake does not support partitioning
Explanation: Delta Lake provides ACID transactions ensuring data consistency, time travel for versioned data access (crucial for reproducible training), schema enforcement preventing corrupt data, and efficient upserts/merges for feature table updates.
9In MLflow, which concept groups multiple runs together to organize related experiments?
A.Run
B.Experiment
C.Artifact
D.Metric
Explanation: An MLflow Experiment is a logical container that groups related runs (training iterations) together. Each experiment has a unique name and ID, making it easy to compare multiple runs with different parameters for the same ML task.
10Which approach should be used to serve a trained ML model as a real-time REST API endpoint on Databricks?
A.Schedule a batch inference job with Databricks Workflows
B.Deploy the model to a Model Serving endpoint
C.Write results to a Delta table using Spark Structured Streaming
D.Use Databricks SQL to query the model
Explanation: Databricks Model Serving provides managed REST API endpoints for real-time model inference. Models registered in Unity Catalog or the MLflow Model Registry can be deployed to serving endpoints that handle scaling, monitoring, and low-latency prediction.

About the Databricks ML Professional Exam

The Databricks ML Professional certification validates advanced skills in building scalable ML pipelines with SparkML, managing the ML lifecycle with MLflow, implementing MLOps practices with Databricks Asset Bundles, and deploying production-grade ML systems with comprehensive monitoring and drift detection.

Exam sponsor: Databricks. The requirements and fees below concern the certification or admission exam, separate from our free practice resources.

Questions

59 questions

Time Limit

120 minutes

Passing Score

~70%

Exam / Certification Fees

$200

Exam sponsor website

Reported exam pass rate: Not published. Exam sponsor website

Fees, eligibility, and exam policies can change. Confirm them with the exam sponsor before applying or paying.

Our practice resources: topics covered

We aim to reflect publicly available exam outlines and topic information in our study resources. Coverage, format, and difficulty may differ from the actual exam, and we cannot guarantee that every detail is accurate or current. Confirm exam requirements, fees, and policies with the official exam sponsor.

44 questions (44%)

Model Development

SparkML pipelines, feature engineering, distributed training, hyperparameter tuning, evaluation, and AutoML

44 questions (44%)

MLOps

MLflow tracking and registry, Asset Bundles, CI/CD, testing, monitoring, drift detection, and governance

12 questions (12%)

Model Deployment

Model Serving endpoints, batch inference, streaming inference, pyfunc models, and rollout strategies

Preparing for the Databricks ML Professional Exam

What You Need to Know

  • Passing score: ~70%
  • Exam length: 59 questions
  • Time limit: 120 minutes
  • Exam / certification fees: $200 Official sources

Using Our Practice Resources

  • Work through all 100 available questions
  • Review every answer and explanation
  • Track weak areas and revisit them
  • Use our AI tutor for tough concepts

Databricks ML Professional: Suggested Study Strategy

1Build a complete end-to-end ML project on Databricks: data prep, feature store, training, MLflow tracking, serving, and monitoring
2Master MLflow deeply — autologging, model signatures, pyfunc custom models, and Model Registry with Unity Catalog
3Practice SparkML Pipeline construction with CrossValidator, ParamGridBuilder, and multiple evaluation metrics
4Understand Databricks Asset Bundles (DABs) for deploying ML workflows as code through CI/CD
5Study Lakehouse Monitoring concepts: data drift, prediction drift, KS test, and automated alerting

Frequently Asked Questions

How many questions are on the Databricks ML Professional exam?

The exam has 59 scored multiple-choice questions to be completed in 120 minutes. Additional unscored items may be included for statistical purposes.

What is the passing score for Databricks ML Professional?

Databricks does not publish an exact passing score, but it is widely reported as approximately 70%. Always check the latest exam guide on databricks.com for current details.

How much does the Databricks ML Professional exam cost?

The exam registration fee is $200 USD per attempt. The registration is valid for 12 months from purchase.

What are the exam domains and their weightings?

As of the latest exam guide, the three domains are Model Development (44%), MLOps (44%), and Model Deployment (12%). MLflow, SparkML, Feature Store, and Lakehouse Monitoring are core topics.

Is there a prerequisite for the ML Professional exam?

There are no strict prerequisites, but Databricks recommends 1+ years of hands-on experience performing ML tasks on the Databricks platform. The ML Associate certification is a useful stepping stone.

How should I prepare for Databricks ML Professional in 2026?

Focus on hands-on practice with MLflow (tracking, registry, autologging), SparkML pipelines, Feature Store, Databricks Asset Bundles, and Lakehouse Monitoring. Build an end-to-end ML project on Databricks covering data prep through deployment and monitoring.