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

Topic	Details
Topic 1	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 2	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 3	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 4	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 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 JIT feature values as a need for real-time deployment Describe how to list all webhooks and how to delete a webhook
Topic 8	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 9	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 10	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 11	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift

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Databricks Certified Machine Learning Professional Sample Questions (Q107-Q112):

NEW QUESTION # 107
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: A

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 # 108
Which of the following is a benefit of logging a model signature with an MLflow model?

A. The model can be deployed using real-time serving tools
B. The schema of input data will be converted to match the signature
C. The model will have a unique identifier in the MLflow experiment
D. The model will be secured by the user that developed it
E. The schema of input data can be validated when serving models

Answer: B

NEW QUESTION # 109
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 PUT in the call to http request
B. Replace list with view in the endpoint URL
C. Replace POST with GET in the call to http request
D. Replace list with webhooks in the endpoint URL
E. There are no necessary changes

Answer: C

NEW QUESTION # 110
Which of the following MLflow operations can be used to automatically calculate and log a Shapley feature importance plot?

A. client.log_artifact
B. mlflow.shap
C. mlflow.shap.log_explanation
D. None of these operations can accomplish the task.
E. mlflow.log_figure

Answer: B

NEW QUESTION # 111
A data scientist has developed a model model and computed the RMSE of the model on the test set. They have assigned this value to the variable rmse. They now want to manually store the RMSE value with the MLflow run.
They write the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so the code block can successfully complete the task?

A. log_model
B. log_param
C. There is no way to store values like this.
D. log_artifact
E. log_metric

Answer: D

NEW QUESTION # 112
......

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