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

Topic	Details
Topic 1	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
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	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 4	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 5	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 6	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
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

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Databricks Certified Machine Learning Professional Sample Questions (Q57-Q62):

NEW QUESTION # 57
Which of the following Databricks-managed MLflow capabilities is a centralized model store?

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

Answer: D

NEW QUESTION # 58
A Machine Learning Engineer uses Lakehouse Monitoring to track their credit scoring model's performance. The existing profile metrics table contains three aggregate metrics:
- adefault_risk_score
- payment_history_score
- credit_utilization_score
They need to:
1. Create a composite risk rating that combines these three scores using weights of 0.5, 0.3, and 0.2 respectively.
2. Monitor drift of this composite score against an established baseline.
Which approach should be used to implement both requirements within Lakehouse Monitoring?

A. Create an aggregate metric for composite_risk_rating and configure a separate drift metric to monitor changes.
B. Create two separate aggregate metrics: one for the composite score calculation and another for drift detection.
C. Create a derived metric for the composite_risk_rating calculation and create a drift metric on this derived metric.
D. Use a scheduled notebook to calculate the composite score and manually insert both the score and drift values into the profile metrics table.

Answer: C

Explanation:
Lakehouse Monitoring supports derived metrics that are computed from existing profile metrics using custom expressions. By defining a derived metric for the composite_risk_rating using the specified weights, the composite score becomes a first-class metric in the monitoring framework.
A drift metric can then be directly configured on this derived metric to compare current values against the baseline, fulfilling both the composite calculation and drift monitoring requirements in a native, governed way.

NEW QUESTION # 59
A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks. Which location in Databricks will show these data visualizations?

A. The Artifacts section of the MLflow Experiment page
B. The MLflow Model Registry Model paqe
C. The Figures section of the MLflow Run page
D. Logged data visualizations cannot be viewed in Databricks
E. The Artifacts section of the MLflow Run page

Answer: C

NEW QUESTION # 60
Which of the following lists all of the model stages are available in the MLflow Model Registry?

A. Development. Staging. Production. Archived
B. None. Staging. Production. Archived
C. Staging. Production. Archived
D. None. Staging. Production
E. Development. Staging. Production

Answer: E

NEW QUESTION # 61
A machine learning engineer wants to deploy a model for real-time serving using MLflow Model Serving. For the model, the machine learning engineer currently has one model version in each of the stages in the MLflow Model Registry. The engineer wants to know which model versions can be queried once Model Serving is enabled for the model.
Which of the following lists all of the MLflow Model Registry stages whose model versions are automatically deployed with Model Serving?

A. Staging. Production
B. None. Staging. Production. Archived
C. Production
D. Staging. Production. Archived
E. [None. Staging. Production

Answer: A

NEW QUESTION # 62
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