Everyone studies differently, so ActualCollection offers the Databricks Certified Machine Learning Professional prep material in three formats: a printable PDF, a desktop test engine for Windows, and an online test engine that runs in any browser. All three carry the same 193 practice questions.
Databricks Databricks-Machine-Learning-Professional Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Machine Learning Professional |
| Exam Number: | Databricks-Machine-Learning-Professional |
| Available Languages: | English |
| Related Certifications: | Databricks Certified Machine Learning Associate |
| Passing Score: | Not publicly disclosed |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | Approximately 45–60 |
| Exam Format: | Multiple select, Scenario-based questions, Multiple choice |
| Certificate Validity Period: | 2 years |
| Exam Price: | $200 USD |
| Recommended Training: | Databricks Academy Machine Learning Training |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: |  |
| Exam Way: | Online proctored exam (typically delivered via Databricks certification partners such as Certiverse or Pearson VUE depending on region and current program structure) |
| Pre Condition: | Recommended experience with Databricks platform and machine learning workflows; Databricks Certified Machine Learning Associate certification is often recommended but not strictly required. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification |
Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Machine Learning Workflow on Databricks | - End-to-end ML pipelines
|
| Topic 2: Model Deployment and Serving | - Model deployment strategies
|
| Topic 3: MLOps, Monitoring, and Governance | - Governance and compliance
|
| Topic 4: MLflow and Experiment Tracking | - Model registry
|
| Topic 5: Machine Learning Models and Algorithms | - Supervised learning methods
|
Common Questions About the Databricks Certified Machine Learning Professional Exam
The Databricks Certified Machine Learning Professional exam is the official Databricks test registered under exam code Databricks-Machine-Learning-Professional. Passing it earns you the Databricks Certified Machine Learning Professional certification, a credential at the Professional level. It is also linked to the related certification: Databricks Certified Machine Learning Associate. Databricks exams are valued because they test job-ready skills, so a passing score here carries real weight on a resume.
The Databricks Certified Machine Learning Professional exam includes Approximately 45–60 questions to be completed within 120 minutes. Do the pacing math before exam day: with that many items on the clock, you need a steady rhythm and the discipline to flag a hard question and move on instead of stalling. Two or three full timed sessions with the ActualCollection test engine will show you exactly what that pace feels like, so time pressure stops being a factor on the real day.
To pass the Databricks Certified Machine Learning Professional exam you need Not publicly disclosed, and the official registration fee is $200 USD. A retake is not discounted: a failed attempt means paying the full $200 USD again, so treat your first sitting as the expensive one. A sensible rule is to book your seat only after you are scoring comfortably above the passing mark on the ActualCollection practice tests, not just squeaking past it once.
Recommended experience with Databricks platform and machine learning workflows; Databricks Certified Machine Learning Associate certification is often recommended but not strictly required.
Eligibility rules do change from time to time, so confirm the current requirements before you register on the official exam page.
Registration for the Databricks Certified Machine Learning Professional exam goes through the official channels below.
As for the delivery format, the exam is taken Online proctored exam (typically delivered via Databricks certification partners such as Certiverse or Pearson VUE depending on region and current program structure).
Databricks points candidates toward the following training options for Databricks Certified Machine Learning Professional.
Course work builds the foundation; question practice makes it stick. The 193 practice questions in the ActualCollection Databricks-Machine-Learning-Professional package let you rehearse each topic under exam-style pressure before the real thing.
Yes. ActualCollection offers a free PDF demo of the Databricks Certified Machine Learning Professional material so you can judge the question quality and format before spending anything. After purchase, your license includes 365 days of free updates, and if you want to keep receiving updates after that period, renewals are available at a 50% discount.
If you take the Databricks Certified Machine Learning Professional exam within 60 days of your purchase and do not pass, ActualCollection backs you with a 100% money-back guarantee. The claim must match the exam your product covers: attempts taken within 3 days of purchase are not eligible (that is too little preparation time), and neither are downloaded-but-unused products, free materials, or expired orders. The candidate name must match the payer name, and you need to submit a scanned enrollment slip plus the official Score Report PDF within 2 days of the exam; claims are processed within 7 days. Prefer not to refund? You can swap instead and receive two other exam products of equal value for free while keeping the update service on your original purchase.
Delivery itself is instant: your files are downloadable right away and emailed to you within one minute of payment. If nothing arrives within 2 hours, contact customer service. There is no limit on how many computers you may install the software on.
The official Databricks Certified Machine Learning Professional syllabus is organized into 5 domains. Key areas include Model Deployment and Serving, Machine Learning Workflow on Databricks, and Machine Learning Models and Algorithms. The complete, up-to-date topic list appears in the exam topics section above; work through it line by line and flag anything you cannot yet explain in your own words.
Databricks Certified Machine Learning Professional Sample Questions:
Why is Apache Spark useful for machine learning training?
- A. Web serving
- B. Distributed data processing
- C. GPU rendering
- D. Data visualization
Correct Answer: B 🗳️
Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).
A machine learning engineer wants to deploy a model for real-time serving using MLflow Model Serving. For the model, the machine learning engineer currently has one model version in each of the stages in the MLflow Model Registry. The engineer wants to know which model versions can be queried once Model Serving is enabled for the model. Which of the following lists all of the MLflow Model Registry stages whose model versions are automatically deployed with Model Serving?
- A. Staging, Production
- B. None, Staging, Production, Archived
- C. Production
- D. None, Staging, Production
- E. Staging, Production, Archived
Correct Answer: A 🗳️
A machine learning engineer has developed a model and registered it using the FeatureStoreClient fs. The model has model URI model_uri. The engineer now needs to perform batch inference on customer-level Spark DataFrame spark_df, but it is missing a few of the static features that were used when training the model. The customer_id column is the primary key of spark_df and the training set used when training and logging the model.
Which of the following code blocks can be used to compute predictions for spark_df when the missing feature values can be found in the Feature Store by searching for features by customer_id?
- A. fs.score_model(model_uri, spark_df)
- B. fs.score_batch(model_uri, spark_df)
- C. df = fs.get_missing_features(spark_df, model_uri)
fs.score_batch(model_uri, df) - D. df = fs.get_missing_features(spark_df)
fs.score_batch(model_uri, df) - E. df = fs.get_missing_features(spark_df, model_uri)
fs.score_model(model_uri, df)
Correct Answer: B 🗳️
A Machine Learning Engineer is responsible for maintaining a fraud detection model deployed on Databricks. They want to implement a retraining pipeline that automatically starts when the model's F1 score drops below a threshold or when input feature distributions change significantly.
Which two actions should the engineer take to implement this automated retraining? (Choose two.)
- A. Set up a manual retraining schedule to run every week regardless of alerts.
- B. Use Databricks SQL to create alerts on model performance and data drift metrics stored in Delta tables.
- C. Schedule a recurring query on the Lakehouse monitoring table.
- D. Use MLflow to manually log metrics and retrain the model offline.
- E. Configure these alerts to send webhook notifications that trigger the model training job.
Correct Answer: B,E 🗳️
Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).
A Machine Learning Engineer is considering moving their functions and unit tests from notebooks into separate Python files (e.g., modules and test scripts) to take advantage of the numerous benefits of this approach like automated execution, code reusability, and version control. Which challenge should the engineer consider with this approach?
- A. Managing a more complex project structure can be harder to maintain and navigate.
- B. Separating code and tests often leads to decreased reliability and poor code quality.
- C. This change would prevent the use of any non-Python environments like Scala.
- D. It makes functions harder to import and reuse across different notebooks.
Correct Answer: A 🗳️
Explanation: Only visible for ActualCollection members. You can sign-up / login (it's free).
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