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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	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 which code block will trigger a shown webhook Describe the basic purpose and user interactions with Model Registry
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	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 6	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 7	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 8	Identify less performant data storage as a solution for other use cases Describe why complex business logic must be handled in streaming deployments
Topic 9	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 10	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 11	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow

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Databricks Certified Machine Learning Professional Sample Questions (Q149-Q154):

NEW QUESTION # 149
A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the original model object.
Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?

A. client.list_artifacts(run_id)["feature-importances.csv"]
B. mlflow.load_model(model_uri)
C. client.pyfunc.load_model(model_uri)
D. mlflow.sklearn.load_model(model_uri)
E. This can only be viewed in the MLflow Experiments UI

Answer: B

NEW QUESTION # 150
Which is a benefit of logging an input example with an MLflow model?

A. An example record is available to test serving applications
B. The model evaluation metrics are stored with the model
C. The environment requirements are packaged with the MLmodel file
D. An example prediction is available to test post-inference pipelines

Answer: A

Explanation:
Logging an input example with an MLflow model provides a concrete sample of the data format expected by the model. This helps serving applications and users understand the model's input structure and verify that inference requests are properly formatted when deploying or testing the model in production.

NEW QUESTION # 151
Which Spark ML class supports automated hyperparameter tuning?

A. PipelineModel
B. BinaryClassificationEvaluator
C. CrossValidator
D. ParamGridBuilder

Answer: C

Explanation:
CrossValidator performs:
parameter search
k-fold cross validation.

NEW QUESTION # 152
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 # 153
A machine learning engineer wants to delete an active MLflow Model Registry Webhook with webhook ID webhook_id for a specific model.
They are using the following code block:

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

A. There are no necessary changes
B. Replace list with webhooks in the endpoint URL
C. Replace DELETE with POST in the call to http_request
D. Replace list with delete in the endpoint URL

Answer: D

Explanation:
To delete a webhook using the MLflow REST API, the correct endpoint is:
/api/2.0/mlflow/registry-webhooks/delete
In the provided code, the endpoint incorrectly uses "list", which retrieves webhooks rather than deleting them. The HTTP method "DELETE" is correct, so the only necessary change is to replace list with delete in the endpoint URL.

NEW QUESTION # 154
......

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