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Databricks Databricks-Machine-Learning-Professional Exam Overview:

Certification Vendor:	Databricks
Exam Name:	Databricks Certified Machine Learning Professional
Exam Number:	Databricks-Machine-Learning-Professional
Certificate Validity Period:	2 years
Real Exam Qty:	60
Exam Price:	USD 200
Related Certifications:	Databricks Certified Machine Learning Associate
Available Languages:	English
Exam Duration:	120 minutes
Exam Format:	Multiple choice
Passing Score:	70%
Sample Questions:	Databricks Databricks-Machine-Learning-Professional Sample Questions
Exam Way:	Online (proctored) or Test Center
Pre Condition:	No formal prerequisites, but 1+ years of hands-on experience performing the machine learning tasks outlined in the exam guide is highly recommended. Recommended courses: Machine Learning at Scale and Advanced Machine Learning Operations (instructor-led or self-paced via Databricks Academy).
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	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 3	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 4	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 5	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
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 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 8	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

Databricks Certified Machine Learning Professional Sample Questions (Q185-Q190):

NEW QUESTION # 185
A Machine Learning Engineer has a real-time fraud detection model deployed that approves or blocks millions of transactions daily. They need to deploy a new version of the model with improved detection accuracy to this high-traffic, business-critical application. Because any model downtime could result in lost revenue or customer dissatisfaction, the engineer must ensure zero downtime and minimal disruption for end users. Leadership also requires that any rollback to the previous version be immediate if issues are detected with the new model in production. Which deployment strategy meets these requirements?

A. Use a canary deployment by initially routing a small percentage of user traffic to the new model version, monitoring results, and gradually increasing exposure until all traffic uses the new version if no problems are detected.
B. Deploy the new model in parallel with the old version, monitor both for a set period, and then notify all stakeholders to manually switch over to the new version at a coordinated time.
C. Replace the current production model with the new version during a scheduled maintenance window, notify affected users of potential brief disruptions, and prepare to reroute requests to a backup if failures occur after deployment.
D. Use a blue-green deployment for the new model, maintaining two separate production environments (one "blue," one "green") and switching user traffic to the new version only after confirming it is healthy, enabling instant rollback if needed.

Answer: D

Explanation:
A blue-green deployment maintains two fully operational production environments and shifts traffic between them instantly. This approach provides zero downtime during deployment and allows immediate rollback to the previous model version if issues arise, which is critical for high- traffic, business-critical real-time systems.

NEW QUESTION # 186
Concept drift is when there is a change in which element?

A. The distribution of an input variable
B. The relationship between input variables and target variables
C. The distribution of the predicted target given by the model
D. The distribution of a target variable

Answer: B

Explanation:
Concept drift occurs when the underlying relationship between input features and the target variable changes over time. This means that even if the feature and label distributions remain stable, the way features influence the target (the predictive relationship) has shifted, leading to reduced model accuracy unless retraining occurs.

NEW QUESTION # 187
A Machine Learning Engineer is conducting hyperparameter tuning for multiple XGBoost models using Ray Tune on Databricks. They want to integrate MLflow tracking to monitor their experiments and need to ensure proper authentication. The engineer has Ray 2.41 installed and wants to use both Ray Tune and MLflow together in their distributed tuning workflow. They have to configure Databricks to run the hyperparameter optimization with MLflow integration. Which set of configuration steps will do this?

A. Configure DATABRICKS_HOST and DATABRICKS_TOKEN environment variables before calling setup_ray_cluster().
B. Install the MLflow Ray plugin using %pip install mlflow-ray and configure the workspace connection.
C. Enable MLflow autologging with mlflow.ray.autolog() and set the tracking server URI.
D. Set MLFLOW_TRACKING_URI and MLFLOW_EXPERIMENT_TD environment variables before initializing Ray.

Answer: A

Explanation:
When using Ray Tune with MLflow on Databricks, Ray workers must be able to authenticate back to the Databricks workspace to log runs to MLflow Tracking. Setting the DATABRICKS_HOST and DATABRICKS_TOKEN environment variables before initializing the Ray cluster ensures all Ray processes can securely communicate with Databricks and correctly log MLflow experiments during distributed hyperparameter tuning.

NEW QUESTION # 188
A machine learning engineer has created a webhook with the following code block:

Which of the following code blocks will trigger this webhook to run the associate job?

Answer: B

NEW QUESTION # 189
What tool helps avoid feature mismatch between training and inference?

A. Spark SQL
B. Model Registry
C. MLflow Projects
D. Feature Store

Answer: D

Explanation:
Feature Store ensures:
same feature computation logic
consistency between training and serving.

NEW QUESTION # 190
......

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