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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
Available Languages:	English
Passing Score:	Not publicly disclosed (approximately 70%)
Real Exam Qty:	59
Related Certifications:	Databricks Certified Machine Learning Associate
Exam Format:	Multi-select, Multiple choice
Exam Price:	$200 USD
Exam Duration:	120 minutes
Certificate Validity Period:	2 years
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 the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
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	Identify JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 4	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 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 concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 7	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 8	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 9	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 10	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments

Databricks Certified Machine Learning Professional Sample Questions (Q58-Q63):

NEW QUESTION # 58
A Machine Learning Engineer has deployed a fraud detection model that processes 10,000 transactions per hour. The model was trained on data from Q1 2024, but it's now Q4 2024. The ML team notices three concerning trends: (1) the model's precision has dropped from 92% to
78% over the past month, (2) the average transaction amount in recent data has increased from
$150 to $220 and (3) the relationship between transaction frequency and fraud likelihood has weakened significantly due to new payment methods being introduced. The engineer needs to implement a monitoring solution that can detect the root cause of the performance degradation, identify why the precision dropped, and be able to do this at the scale needed. Which monitoring pipeline component will do this?

A. Model Health Monitoring Pipeline because it ensures the infrastructure can handle the 10,000 transactions per hour load.
B. Model Performance Monitoring Pipeline because it directly tracks precision metrics and can alert when thresholds are breached.
C. Logging and Monitoring Pipeline because it captures all prediction requests and can identify patterns in the fraud predictions.
D. Drift Detection Pipeline because it can identify both the data drift (transaction amount changes) and concept drift (weakened relationship between features and target).

Answer: D

Explanation:
A drift detection pipeline is designed to diagnose the root causes of model performance degradation at scale. It can detect data drift, such as changes in the distribution of transaction amounts, and concept drift, where the relationship between input features and the fraud label changes due to new payment methods. This directly explains why precision dropped and provides actionable insight beyond simply observing metric degradation.

NEW QUESTION # 59
A Machine Learning Engineer needs to develop a custom anomaly detection model that monitors the internal IT infrastructure of their company. The model takes in compute metrics, logs, and user data and generates a binary prediction. The engineer plans to deploy it as a Databricks Model Serving endpoint. In production there will only be one client calling the endpoint once every
15 seconds. Leadership sees the model as an important part of their operational improvement strategy so maintaining consistent, stable, low latency inference is a requirement while minimizing infrastructure costs. The engineer plans to deploy the endpoint via the MLflow Deployment SDK.
Which endpoint config for the MLflow Deployment SDK should the engineer select?

Answer: C

Explanation:
With a single client making requests every 15 seconds, the traffic is steady and predictable, and low-latency inference is a strict requirement. Disabling scale-to-zero avoids cold start latency, ensuring consistent response times. Selecting a Small workload size minimizes infrastructure costs while still providing sufficient resources for a lightweight binary classification model, making this configuration the best balance between performance, stability, and cost.

NEW QUESTION # 60
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. Which code block can they use to accomplish the task?

Answer: C

Explanation:
The correct method for transitioning a model version to a new stage using the MLflow Client is client.transition_model_version_stage(name, version, stage). This updates the stage of a specific model version in the Model Registry, such as moving it from "Staging" to "Production". The stage parameter specifies the target stage only -- there is no from argument.

NEW QUESTION # 61
A data scientist has computed updated feature values for all primary key values stored in the Feature Store table features. In addition, feature values for some new primary key values have also been computed. The updated feature values are stored in the DataFrame features_df. They want to replace all data in features with the newly computed data. Which of the following code blocks can they use to perform this task using the Feature Store Client fs?

Answer: B

NEW QUESTION # 62
Which of the following is a simple statistic to monitor for categorical feature drift?

A. Number of unique values
B. Mode
C. Mode, number of unique values, and percentage of missing values
D. None of these
E. Percentage of missing values

Answer: C

NEW QUESTION # 63
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