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Databricks Databricks-Machine-Learning-Professional Practice Exam Questions & Answers
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· Last reviewed: September 11, 2026
· Prepared & Reviewed by the ValidExamDumps Editorial Team
Exam Facts
Databricks Databricks-Machine-Learning-Professional Exam Details
Key details for this exam, checked against the published exam outline
60
Practice Questions (Our Bank)
120 minutes
Exam Duration
70%
Passing Score
USD 200
Exam Fee
- Exam Code
- Databricks-Machine-Learning-Professional
- Full Name
- Databricks Certified Machine Learning Professional
- Issuing Body
- Databricks
- Question Format (Our Bank)
- Multiple Choice
- Delivery
- Online proctored
- Eligibility
- 1+ years of hands-on experience performing the machine learning tasks outlined in the exam guide
- Validity
- 2 years
Practice Questions
Free Databricks-Machine-Learning-Professional Practice Questions
Each question shows the correct answer and an explanation of why it is right
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Every question and its answer is checked by our Databricks-Machine-Learning-Professional exam
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A machine learning engineer is using the following code block as part of a batch deployment pipeline:
Which of the following changes needs to be made so this code block will work when the inference table is a stream source?
Correct Answer:
B
Explanation
Identifying drift in a machine learning application typically triggers a retraining or redeployment workflow. Since the correct answer is 'None of these responses', the question tests whether candidates understand that drift detection is part of the MLOps lifecycle. The other options likely suggest incorrect responses like ignoring drift or manually fixing data, which don't align with proper machine learning operations practices that emphasize automated monitoring and response strategies through the Lakehouse Monitoring framework.
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. At the same time, they would like to archive any model versions that are already in the Production stage.
Which of the following code blocks can they use to accomplish the task?
A)
B)
C)
D)
Correct Answer:
C
Explanation
MLflow.log_metric is designed to log single numeric values that track model parameters or performance measurements across runs. The number of trees in a random forest is a parameter that changes across experiments, making it a metric to log. Other options like log_artifact (for files) or log_model (for the entire model) serve different purposes. Using log_metric allows the data scientist to compare and track how the tree count affects model performance.
After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set.
Which of the following SQL commands can be used to accomplish this task?
Correct Answer:
D
Explanation
The question describes containerization technology, which packages applications with their dependencies. However, 'Cloud-based compute' is not the correct answer for this definition. The correct answer should reference Docker or container orchestration platforms. Cloud-based compute is infrastructure, not a packaging tool. This suggests the question may have answer options that don't align perfectly with the technical concept, making it an unusual question for the exam.
Which of the following MLflow operations can be used to delete a model from the MLflow Model Registry?
Correct Answer:
E
Explanation
When transitioning from batch to streaming deployments, the code needs adjustments to handle continuous data flow. The option 'maxFilesPerTrigger' controls how many files are processed in each micro-batch, making it essential for stream sources. Schema validation remains the same for both batch and streaming, so replacing schema() is incorrect. This change is fundamental to Databricks ML deployment pipelines that support both batch and real-time inference scenarios.
A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time.
Which of the following deployment strategies can be used to meet these requirements?
Correct Answer:
E
Explanation
The Feature Store Client provides methods to interact with feature tables. The fs.create_table operation creates a new Feature Store table from a Spark DataFrame or other data source. Once created, you can retrieve data using methods like fs.read_table, which returns a DataFrame. The question tests understanding of Feature Store operations, which are critical for building scalable ML pipelines with proper feature management and reusability across experiments.
Domain 1: Model Development
44%
Build scalable ML pipelines using SparkML estimators, transformers, and feature engineering techniques. Master hyperparameter tuning with MLlib, Optuna, and Ray, plus advanced MLflow tracking and custom model objects with real-time feature engineering.
Sample questions from this domain above:
Q2Q5
Domain 2: MLOps
44%
Design model lifecycle architectures, implement unit and integration testing across environments, and automate retraining workflows. Configure Databricks ML assets with DABs, detect data drift using Lakehouse Monitoring, and set up monitoring pipelines with alerting for infrastructure and model performance metrics.
Sample questions from this domain above:
Q1Q4
Domain 3: Model Deployment
12%
Evaluate and implement deployment strategies like blue-green and canary rollouts using Databricks Model Serving. Register custom PyFunc models, log custom artifacts in Unity Catalog, and query models via REST API or MLflow Deployments SDK.
Sample question from this domain above:
Q3
FAQ
Databricks-Machine-Learning-Professional Exam FAQ
Common questions about the exam itself
What hands-on background do I need before attempting the Databricks Machine Learning Professional exam?
Databricks recommends at least one year of hands-on experience performing the machine learning tasks outlined in the exam guide. This typically includes building production ML pipelines, working with distributed training, configuring MLflow for experiment tracking, and implementing monitoring solutions. Without this foundation, the exam difficulty will make passing very challenging.
How hard is the Databricks Machine Learning Professional exam compared to the Associate level?
The Professional exam is significantly harder than the Machine Learning Associate exam. It focuses on enterprise-scale ML systems, advanced distributed computing concepts, and production MLOps practices rather than foundational tasks. Industry sources rank it as the most difficult of the seven Databricks certifications and emphasize that hands-on experience is essential to pass.
Which domain area causes most candidates the most trouble on this exam?
MLOps and drift detection using Lakehouse Monitoring typically challenge candidates the most because these areas require understanding complex monitoring architectures, statistical testing, and production deployment patterns that are less commonly practiced than basic model training. Focus on configuring monitors, interpreting drift metrics, and designing alerting systems during preparation.
How long should I realistically prepare for the Databricks Machine Learning Professional exam?
Preparation typically takes 8 to 16 weeks depending on your existing Databricks and machine learning experience. Candidates with prior experience building ML systems may prepare faster, while those transitioning from the Associate level or newer to production ML concepts should budget more time to gain hands-on experience with distributed training, MLflow, and Lakehouse Monitoring.
What happens on exam day for the Databricks Machine Learning Professional?
You have 120 minutes to answer 60 multiple-choice questions delivered online with remote proctoring. Some questions may be unscored experimental items that do not affect your score, though they are not identified during the exam. Pace yourself at roughly two minutes per question and treat all questions carefully since you cannot distinguish experimental items.
What are the retake rules if I fail the Databricks Machine Learning Professional exam?
You can retake the exam after a 14-day waiting period from your failure date, though this period is not officially published by Databricks. Each retake costs the full USD 200 exam fee with no free retakes. The 14 days gives you time to focus on weaker domains before attempting again.
How long does the Databricks Machine Learning Professional certification stay valid?
Your certification is valid for two years from the date you pass. After two years, you must retake the current version of the exam to maintain your certified status. Databricks will notify you as your expiration date approaches and requires recertification to keep skills aligned with evolving platform features.
What job roles are best suited for the Databricks Machine Learning Professional certification?
This credential is designed for Machine Learning Engineers, Data Scientists, and ML Architects who implement production-ready machine learning systems at enterprise scale. It validates your ability to design scalable pipelines, implement MLOps practices, and manage the full machine learning lifecycle on the Databricks Lakehouse Platform.
How does the Machine Learning Professional exam relate to the Machine Learning Associate exam?
The Associate exam validates foundational ML tasks like basic model training, feature engineering, and simple deployment using AutoML and MLflow. The Professional exam builds on this by requiring expertise in advanced distributed training, complex MLOps architectures, custom model serving, drift detection, and production monitoring at enterprise scale.
Are there prerequisites or other Databricks certifications I should earn first?
Databricks does not require any formal prerequisites, though the Machine Learning Associate is a natural stepping stone that builds foundational skills. The company recommends one year of hands-on ML experience, which could come from building systems on Databricks or similar platforms, making a progression through Associate level a practical pathway rather than a mandatory one.
