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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 Format:	Multi-select, Multiple choice
Available Languages:	English
Exam Price:	$200 USD
Passing Score:	Not publicly disclosed (approximately 70%)
Certificate Validity Period:	2 years
Real Exam Qty:	59
Exam Duration:	120 minutes
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	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
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	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 4	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
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	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 7	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 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 (Q171-Q176):

NEW QUESTION # 171
A Machine Learning Engineer has a production AI model that predicts fraudulent transactions in real time. This model is integrated into critical business workflows and any downtime could result in significant financial loss and regulatory penalties. The engineer needs to deploy a new, improved version of the model to production to replace the existing model. The bank's fairness policy requires that all transactions be exposed to the same model at any given time. Additionally, the deployment must meet the following requirements:
- Zero downtime: The fraud detection service must remain continuously
available to users and downstream systems.
- Immediate rollback: If the new model causes issues, you must be able
to revert to the previous version instantly.
Which deployment strategy meets these requirements?

A. Canary
B. Blue-Green
C. Rolling
D. Shadow

Answer: B

Explanation:
Blue-green deployment maintains two fully operational production environments and switches all traffic from the old model to the new model at once. This guarantees zero downtime and ensures that all transactions are handled by the same model version at any given time. If issues arise, traffic can be immediately switched back to the previous environment, enabling instant rollback while meeting strict fairness and availability requirements.

NEW QUESTION # 172
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. 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.
B. 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.
C. 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.
D. 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.

Answer: B

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 # 173
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 metrics with model
B. Replace log_metric with log_metrics
C. Replace mlflow.log_metric with mlflow.sklearn.log_metric
D. Replace metrics with rmse, mae, r2

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 # 174
Which of the following Databricks-managed MLflow capabilities is a centralized model store?

A. Experiments
B. Models
C. Feature Store
D. Model Registry
E. Model Serving

Answer: E

NEW QUESTION # 175
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: D

NEW QUESTION # 176
......

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