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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 Duration: | 120 minutes |
| Exam Format: | Multi-select, Multiple choice |
| Certificate Validity Period: | 2 years |
| Available Languages: | English |
| Passing Score: | Not publicly disclosed (approximately 70%) |
| Real Exam Qty: | 59 |
| Exam Price: | $200 USD |
| Related Certifications: | Databricks Certified Machine Learning Associate |
| 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 |
|---|---|---|
| Topic 1: Model Development | 44% | - Advanced MLflow usage - Feature Store and automated feature pipelines - Scalable ML pipelines with SparkML - Distributed training and hyperparameter tuning |
| Topic 2: ML Ops | 44% | - Model monitoring and drift detection with Lakehouse Monitoring - Testing and validation strategies - Automated retraining workflows - Environment management with Databricks Asset Bundles |
| Topic 3: Model Deployment | 12% | - Model rollout and version management - Deployment strategies - Custom model serving |
Databricks-Machine-Learning-Professional Exam FAQ: Read This Before You Register
Databricks Certified Machine Learning Professional is an official Databricks certification exam, identified by exam code Databricks-Machine-Learning-Professional. Passing earns you the Databricks Certified Machine Learning Professional certification, classified at the Professional level. It also connects with Databricks Certified Machine Learning Associate, so it can serve as a stepping stone in a wider certification plan. In hiring, certificates work as a quick, quantitative standard, and this one tells employers your skills passed the vendor's own measurement.
Databricks Certified Machine Learning Professional candidates answer 59 questions within 120 minutes. Efficiency under the clock is a trainable skill, not a talent: decide your per-question budget in advance, flag anything that threatens it, and keep moving. Running timed simulations in the Prep4away test engine until you finish with time to spare is the most reliable way to make exam day feel controlled rather than rushed.
The Databricks Certified Machine Learning Professional exam requires Not publicly disclosed (approximately 70%) to pass, and official registration costs $200 USD. Since retakes bill the full $200 USD again, the economic argument for thorough preparation writes itself. Let the Prep4away practice tests be your measuring stick: when your scores hold above the passing line across several consecutive sessions, the exam stops being a financial risk.
No mandatory prerequisites; recommended: 6+ months hands-on experience with Databricks ML, SparkML, MLflow, and Python
Because vendors adjust their policies, verify the current requirements before registering on the official exam page.
Registration for Databricks Certified Machine Learning Professional goes through the official channels below.
For planning purposes, note that the exam is delivered Online proctored or in-person test center.
Databricks recommends these training options for Databricks Certified Machine Learning Professional candidates.
Whatever training path you take, finish it with question practice: the 193 items in the Prep4away Databricks-Machine-Learning-Professional package are where knowledge turns into exam-day scoring ability.
Yes. Prep4away provides a free PDF demo of the Databricks Certified Machine Learning Professional questions, so you can evaluate the material before committing. After purchase, updates are free for 365 days, and when your product expires you can extend the update service at a 50% discount.
Your purchase is backed by a 100% money-back guarantee with transparent conditions. Take the Databricks Certified Machine Learning Professional exam within 60 days of purchase; if you fail, you qualify for a full refund, provided the exam matches your product. Attempts within 3 days of purchase do not qualify, and neither do downloaded-but-unused products, free materials, or expired orders; the candidate name must match the payer name. File with a scanned enrollment slip and the official Score Report PDF within 2 days of the exam, and claims are processed within 7 days. You may instead choose an exchange: two other exam products of equal value, free, while your original purchase keeps its update service.
Delivery is immediate: files are downloadable at payment and emailed to you within one minute. If nothing arrives within 2 hours, check spam and contact customer service. Installation is unlimited across your computers.
The Databricks Certified Machine Learning Professional syllabus is organized into 3 domains, with the largest being ML Ops (44%), Model Deployment (12%), and Model Development (44%). The complete breakdown appears in the topics section above; a clear classification of what the exam tests is the first step toward efficient preparation.
Databricks Certified Machine Learning Professional Sample Questions:
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. Separating code and tests often leads to decreased reliability and poor code quality.
- B. This change would prevent the use of any non-Python environments like Scala.
- C. It makes functions harder to import and reuse across different notebooks.
- D. Managing a more complex project structure can be harder to maintain and navigate.
Correct Answer: D 🗳️
Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).
A Machine Learning Engineer is building an application that requires low latency data lookups in response to a user's question following a RAG based search. They want to ensure their users can receive as recent data as possible for urgent requests, so data should not be more than a few minutes late. The underlying data is a large table that may contain hundreds of gigabytes of data. Which data serving approach will suit their use case?
- A. Online tables with continuous sync mode
- B. A fast database hosted in MLflow model serving
- C. Online tables with snapshot sync mode
- D. Online tables with triggered mode and a time series key
Correct Answer: A 🗳️
Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).
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 code block can they use to accomplish the task?
- A.

- B.

- C.

- D.

Correct Answer: A 🗳️
A Data Scientist has been performing hyperparameter tuning using Ray Tune with grid search.
After team discussions, they decide to switch to Bayesian optimization to more efficiently explore the parameter space.
Their current code is:
How can they implement this change?
- A.

- B.

- C.

- D.

Correct Answer: D 🗳️
Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).
A machine learning engineer has registered a Spark ML model in the MLflow Model Registry using the Spark ML model flavor with UI model_uri. Which operation can be used to load the model as a Spark ML object for batch deployment?
- A. mlflow.spark.load_model(model_uri)
- B. mlflow.spark.read_model(model_uri)
- C. mlflow.sklearn.load_model(model_uri)
- D. mlflow.pyfunc.load_model(model_uri)
Correct Answer: A 🗳️
Explanation: Only visible for Prep4away members. You can sign-up / login (it's free).
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