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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
Passing Score:	Not publicly disclosed (approximately 70%)
Real Exam Qty:	59
Exam Duration:	120 minutes
Available Languages:	English
Certificate Validity Period:	2 years
Exam Format:	Multiple choice, Multi-select
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:	Databricks Databricks-Machine-Learning-Professional Sample Questions
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

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Databricks Databricks-Machine-Learning-Professional Exam Syllabus Topics:

Topic	Details
Topic 1	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 2	Identify that data can arrive out-of-order with structured streaming Identify how model serving uses one all-purpose cluster for a model deployment
Topic 3	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 4	Identify a use case for HTTP webhooks and where the Webhook URL needs to come Identify advantages of using Job clusters over all-purpose clusters
Topic 5	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 6	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 7	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 8	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 9	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry

Databricks Certified Machine Learning Professional Sample Questions (Q143-Q148):

NEW QUESTION # 143
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. Managing a more complex project structure can be harder to maintain and navigate.
D. It makes functions harder to import and reuse across different notebooks.

Answer: C

Explanation:
Moving code and unit tests into separate Python modules introduces a more structured project layout, which can increase complexity. Engineers must manage directories, dependencies, and imports carefully, making the project slightly harder to navigate and maintain compared to simple notebook-based workflows, especially for teams new to this structure.

NEW QUESTION # 144
A machine learning engineer is monitoring categorical input variables for a production machine learning application. The engineer believes that missing values are becoming more prevalent in more recent data for a particular value in one of the categorical input variables.
Which of the following tools can the machine learning engineer use to assess their theory?

A. Kolmogorov-Smirnov (KS) test
B. One-way Chi-squared Test
C. Jenson-Shannon distance
D. None of these
E. Two-way Chi-squared Test

Answer: B

NEW QUESTION # 145
How can you save a trained Spark ML PipelineModel?

A. pipeline.store()
B. pipeline.save()
C. pipeline.persist()
D. pipelineModel.write().save()

Answer: D

Explanation:
Example:
pipelineModel.write().overwrite().save("/model")

NEW QUESTION # 146
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 SQL command can be used to accomplish this task?

A. DESCRIBE HISTORY
B. DESCRIBE
C. VERSION
D. TIMESTAMP
E. HISTORY

Answer: A

NEW QUESTION # 147
A machine learning engineer has developed a machine learning pipeline that produces a scikit- learn model model and computes the RMSE rmse. MAE mae, and R-squared r2 values for the test set. They now want to log these values with the MLflow run. These values are stored in the dictionary metrics.
They run the following code block:

The code block produces an error.
Which changes to the code block will successfully complete the task?

A. Replace mlflow.log_metric with mlflow.sklearn.log_metric
B. Replace log_metric with log_metrics
C. Replace metrics with rmse, mae, r2
D. Replace metrics with model

Answer: B

Explanation:
The method mlflow.log_metric() logs a single metric, while mlflow.log_metrics() is used to log multiple metrics at once from a dictionary. Since metrics is a dictionary containing rmse, mae, and r2, the correct function is mlflow.log_metrics(metrics).

NEW QUESTION # 148
......

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