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

TopicDetails
Topic 1
  • Identify live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 2
  • 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 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
  • 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 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 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 (Q37-Q42):

NEW QUESTION # 37
A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input. Which of the following MLflow operations can be used to perform this task?

Answer: D


NEW QUESTION # 38
Which of the following is a drawback associated with using Jensen-Shannon (JS) distance for numeric feature drift detection?

Answer: C

Explanation:
A key drawback of using Jensen-Shannon (JS) distance for numeric feature drift detection is that it requires manual threshold tuning to determine what level of divergence indicates significant drift. This makes automation and consistent interpretation challenging, especially across features or datasets with differing distributions.


NEW QUESTION # 39
Which of the following is a simple, low-cost method of monitoring numeric feature drift?

Answer: C


NEW QUESTION # 40
Which statement describes streaming with Spark as a model deployment strategy?

Answer: D


NEW QUESTION # 41
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?

Answer: B


NEW QUESTION # 42
......

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