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Instant Download Databricks : Databricks-Machine-Learning-Professional Questions & Answers as PDF & Test Engine
- Exam Code: Databricks-Machine-Learning-Professional
- Exam Name: Databricks Certified Machine Learning Professional
- Updated: Oct 16, 2026
- No. of Questions: 193 Questions and Answers
- Download Limit: Unlimited
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Databricks Databricks-Machine-Learning-Professional Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Machine Learning Professional Exam |
| Exam Number: | Databricks-Machine-Learning-Professional |
| Exam Format: | Multi-select, Multiple choice |
| Real Exam Qty: | 59 |
| Exam Duration: | 120 minutes |
| Related Certifications: | Databricks Certified Machine Learning Associate |
| Exam Price: | $200 USD |
| Passing Score: | Not publicly disclosed (approximately 70%) |
| Certificate Validity Period: | 2 years |
| Available Languages: | English |
| Recommended Training: | Advanced Machine Learning Operations Machine Learning at Scale |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored or in-person test center |
| Pre Condition: | No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python |
| Official Syllabus URL: | https://www.databricks.com/learn/certification/machine-learning-professional |
Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Model Development | 44% | - Distributed training and hyperparameter tuning - Scalable ML pipelines with SparkML - Feature Store and automated feature pipelines - Advanced MLflow usage |
| ML Ops | 44% | - Testing and validation strategies - Environment management with Databricks Asset Bundles - Automated retraining workflows - Model monitoring and drift detection with Lakehouse Monitoring |
| Model Deployment | 12% | - Deployment strategies - Model rollout and version management - Custom model serving |
Databricks-Machine-Learning-Professional Exam FAQ: Reduce Your Risk, Read This First
Databricks Certified Machine Learning Professional is an official Databricks exam, identified by exam code Databricks-Machine-Learning-Professional. Passing it earns the Databricks Certified Machine Learning Professional certification at the Professional level. It also relates to Databricks Certified Machine Learning Associate. Certifications like this one remain a reliable route to standing out: they prove ability in a way resumes alone cannot.
The Databricks Certified Machine Learning Professional exam contains 59 questions to complete within 120 minutes. The candidates who run out of time are usually the ones who never practiced against a clock. The Exams4sures software engine lets you limit your test time exactly like the real exam, exposing pacing weaknesses while they are still free to fix.
You need Not publicly disclosed (approximately 70%) to pass Databricks Certified Machine Learning Professional, and the official registration fee is $200 USD. Retakes charge the full $200 USD again, which makes failing a genuinely expensive outcome. Reduce that risk the rational way: practice with the Exams4sures questions until your scores sit consistently above the requirement, then book.
No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python
Vendor requirements change from time to time, so verify the current conditions before registering via the official exam page.
Registration for Databricks Certified Machine Learning Professional runs through the official channels below.
One practical note: the exam is delivered Online proctored or in-person test center.
Databricks recommends the following training for Databricks Certified Machine Learning Professional candidates.
Whatever training you take, anchor it with the 193 practice questions in the Exams4sures Databricks-Machine-Learning-Professional package, each with a detailed explanation that turns every mistake into a lesson.
Yes. You can download the free demo of the Databricks Certified Machine Learning Professional questions before you buy, and after purchase you have the right to one year of free updates. When your product expires, extending the update service costs 50% of the regular price. Three versions, PDF, software, and online APP, let you study the way that suits you.
A 100% money-back guarantee protects you under clear conditions. Take the Databricks Certified Machine Learning Professional exam within 60 days of purchase; if you fail, you can claim a full refund, provided the exam matches your product. Attempts within 3 days of purchase are ineligible, as are downloaded-but-unused products, free materials, and expired orders; the candidate name must match the payer name. Submit a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and claims are processed within 7 days. Alternatively, exchange for two other exam products of equal value, free, while keeping the update service on your original purchase.
Delivery is instant: files unlock for download at payment and are automatically emailed within one minute. If nothing arrives within 2 hours, check spam and contact customer service, available 24/7. Installation is unlimited across your computers.
The Databricks Certified Machine Learning Professional syllabus divides into 3 domains. The leading areas are Model Deployment (12%), Model Development (44%), and ML Ops (44%). The complete outline is published above; knowing your shortcomings starts with knowing the syllabus.
Databricks Certified Machine Learning Professional Sample Questions:
A Machine Learning Engineer is tasked with implementing a simple solution, requiring as little code as possible, to monitor a regression model and ensure that the R2 score does not exceed a certain threshold. If the threshold is exceeded, the solution needs to send an email to a distribution list. Which proposed solution meets the criteria?
- A. Create a Workflow that runs on a schedule. The Workflow executes a SQL Query that calculates the R2 score. Use Workflow If/Else condition to execute a Notebook Task that sends the email if the R2 score threshold is exceeded.
- B. Configure Lakehouse Monitoring using the Inference profile type. Create a Workflow that runs on a schedule and executes a SQL query that evaluates the R2 score in the profile table. Use Workflow If/Else condition to execute a Notebook Task that sends the email if the R2 score threshold is exceeded.
- C. Configure Lakehouse Monitoring using the Inference profile type. Create a SQL Alert that runs on a schedule and evaluates if the R2 score in the profile table exceeds the threshold. If so, the Alert should send an email to the configured email notification destination.
- D. Configure Lakehouse Monitoring using the Inference profile type. Create a SQL Query that runs on a schedule and evaluates if the R2 score in the profile table exceeds the threshold. If so, the Alert should send an email to the configured email notification destination.
Correct Answer: C 🗳️
Explanation: Only visible for Exams4sures members. You can sign-up / login (it's free).
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model. Which statement is a benefit of this approach when loading the logged pyfunc model for downstream deployment?
- A. There is no longer a need for pipeline-like machine learning objects
- B. The same preprocessing logic will automatically be applied when calling fit
- C. The same preprocessing logic will automatically be applied when calling predict
- D. The pvfunc model can be used to deploy models in a parallelizable fashion
- E. This approach has no impact when loading the logged Pvfunc model for downstream deployment
Correct Answer: A 🗳️
A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:
The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?
- A.
- B. features_df.write.mode("feature").path("new_table")
- C. features_df.write.mode("fs").path("new_table")
- D.
- E.
Correct Answer: A 🗳️
A Machine Learning Engineer wants to implement MLOps. They currently have three environments: dev, stage, and prod. They have many private PyPI packages that they use to train their models, and they are concerned that their environments will not be consistent between dev, stage, and prod. The engineer needs to follow enterprise scaling and CI/CD best practices to ensure that their environments are consistent. Which approach will do this?
- A. Use the same DBR for ML runtime across environments and %pip install any additional packages at the top of the notebook.
- B. Define an all purpose cluster with the private PyPI packages installed, and use the same cluster config across environments.
- C. Define dependencies for each task in their job(s) running the ML pipeline using Databricks Asset Bundles or Terraform.
- D. Use the same DBR across environments.
Correct Answer: C 🗳️
Explanation: Only visible for Exams4sures members. You can sign-up / login (it's free).
A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project". Which MLflow operation can the machine learning engineer use to accomplish this task?
- A. mlflow.add_model_version
- B. MlflowClient.update_registered_model
- C. The machine learning engineer needs to create an entirely new MLflow Model Registry model
- D. MlflowClient.get_model_version
- E. mlflow.register_model
Correct Answer: B 🗳️
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