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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 Price:	$200 USD
Related Certifications:	Databricks Certified Machine Learning Associate
Certificate Validity Period:	2 years
Available Languages:	English
Exam Duration:	120 minutes
Passing Score:	Not publicly disclosed (approximately 70%)
Real Exam Qty:	59
Exam Format:	Multiple choice, Multi-select
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 which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 2	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 3	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 4	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 5	Test whether the updated model performs better on the more recent data Identify when retraining and deploying an updated model is a probable solution to drift
Topic 6	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 7	Create, overwrite, merge, and read Feature Store tables in machine learning workflows View Delta table history and load a previous version of a Delta table
Topic 8	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines

Databricks Certified Machine Learning Professional Sample Questions (Q46-Q51):

NEW QUESTION # 46
Which deployment paradigm can centrally compute predictions for a single record with exceedingly fast results?

A. Edge/on-device
B. None of these strategies will accomplish the task.
C. Batch
D. Streaming
E. Real-time

Answer: E

NEW QUESTION # 47
A machine learning engineering team has decided that they need to have predictions be made available for querying in continuous, equal-sized increments. A computation can be included in one of the increments when all of its feature values are in the inference Spark DataFrame. Which of the following tools can be used to provide this type of continuous inference?

A. Structured Streaming
B. MLflow
C. Delta Lake
D. Spark UDFs

Answer: A

Explanation:
Structured Streaming in Apache Spark enables continuous inference by processing incoming data in microbatches or continuous increments. It ensures that each computation occurs only when all required feature values are available in the inference DataFrame, supporting real-time or near-real-time prediction pipelines with consistent, equal-sized processing intervals.

NEW QUESTION # 48
A Data Scientist needs to analyze drift detection results from Databricks Lakehouse Monitoring.
The system has generated both profile metrics and drift metrics tables. The scientist needs to identify baseline drift in numerical features by comparing current data against a baseline from 6 months ago. Which combination of table columns and values indicates baseline drift in a numerical feature?

A. log_type = "BASELINE" in profile metrics table with population_stability_index > 0.2 in drift metrics table.
B. drift_type = "BASELINE", chi_squared_test.p_value < 0.05, and js_distance > 0.2.
C. drift_type = "BASELINE", ks_test.p_value < 0.05, and wasserstein_distance > 0.1.
D. window_cmp pointing to baseline time window and tv_distance > 0.5 with any drift_type value.

Answer: C

Explanation:
Baseline drift is identified in the drift metrics table by setting drift_type to BASELINE, and for numerical features the drift statistics are the KS test (ks_test with a p-value indicating a statistically significant distribution change) and a numeric distance metric such as wasserstein_distance.

NEW QUESTION # 49
Which of the following is a probable response to identifying drift in a machine learning application?

A. Retraining and deploying a model on more recent data
B. Sunsetting the machine learning application
C. All of these responses
D. Rebuilding the machine learning application with a new label variable
E. None of these responses

Answer: E

NEW QUESTION # 50
A machine learning engineer is monitoring label values for a production machine learning classification model. The engineer believes that the relative prevalence of the classes is becoming changing in more recent data. Which tool can the machine learning engineer use to assess their theory?

A. Kolmogorov-Smirnov (KS) test
B. Two-way Chi-squared Test
C. Jenson-Shannon distance
D. One-way Chi-squared Test

Answer: D

Explanation:
A One-way Chi-squared Test is used to compare observed class distributions with expected distributions to determine if there has been a significant shift. This makes it suitable for detecting label drift in classification tasks, where the relative prevalence of classes may be changing over time.

NEW QUESTION # 51
......

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