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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	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 3	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 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	Identify which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
Topic 6	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
Topic 7	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments

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Databricks Certified Machine Learning Professional Sample Questions (Q116-Q121):

NEW QUESTION # 116
A machine learning engineer is manually refreshing a model in an existing machine learning pipeline. The pipeline uses the MLflow Model Registry model "project". The machine learning engineer would like to add a new version of the model to "project".
Which of the following MLflow operations can the machine learning engineer use to accomplish this task?

A. mlflow.add_model_version
B. MlflowClient.update_registered_model
C. MlflowClient.get_model_version
D. The machine learning engineer needs to create an entirely new MLflow Model Registry model
E. mlflow.register_model

Answer: B

NEW QUESTION # 117
A data scientist set up a machine learning pipeline to automatically log feature importance data from a CSV with each run. They now want to view the feature importance data in Databricks.
Which location in Databricks will show this data?

A. The Data section of the MLflow Run page/span>
B. The Artifacts section of the MLflow Run page
C. The MLflow Model Registry Model page
D. The Artifacts section of the MLflow Experiment page

Answer: B

Explanation:
When a data scientist logs feature importance data (e.g., as a CSV or JSON file) using MLflow, it is stored as an artifact of that specific run. These artifacts are visible under the Artifacts section of the MLflow Run page in Databricks, where users can view or download the logged files associated with that particular experiment run.

NEW QUESTION # 118
Which of the following tools can assist in real-time deployments by packaging software with its own application, tools, and libraries?

A. Autoscaling clusters
B. Containers
C. None of these tools
D. REST APIs
E. Cloud-based compute

Answer: E

NEW QUESTION # 119
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. Rolling
B. Canary
C. Blue-Green
D. Shadow

Answer: C

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 # 120
A machine learning engineer wants to view all of the active MLflow Model Registry Webhooks for a specific model.
They are using the following code block:

Which of the following changes does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

A. Replace POST with GET in the call to http request
B. Replace list with view in the endpoint URL
C. There are no necessary changes
D. Replace list with webhooks in the endpoint URL
E. Replace POST with PUT in the call to http request

Answer: A

NEW QUESTION # 121
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