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

TopicDetails
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
  • Describe the advantages of using the pyfunc MLflow flavor
  • Manually log parameters, models, and evaluation metrics using MLflow
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 live serving benefits of querying precomputed batch predictions
  • Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 6
  • Identify which code block will trigger a shown webhook
  • Describe the basic purpose and user interactions with Model Registry

Reference: https://www.databricks.com/learn/certification/machine-learning-professional

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Databricks Certified Machine Learning Professional Sample Questions:

A data scientist wants to track the runs of their random forest model. The data scientist is changing the number of trees and the maximum depth of the trees in the forest across each run.
They write the following code block:

Which Python object type does params need to be an instance of?

  • A. array
  • B. PySpark DataFrarne
  • C. list
  • D. dict
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for FreeCram members. You can sign-up / login (it's free).

A data scientist wants to examine the data in the Feature Store table table from the database dev as a Spark DataFrame. They have access to Feature Store Client fs. Which line of code can be used to gel the data from table as a Spark DataFrame?

  • A. fs.get_table("table)
  • B. fs.get_table("dev.table")
  • C. fs.create_table("dev.table")
  • D. fs.read_table("dev.table")
Reveal Solution  Discussion  0

Correct Answer: D  🗳️

Explanation: Only visible for FreeCram members. You can sign-up / login (it's free).

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 MLflow operation can the machine learning engineer use to accomplish this task?

  • A. mlflow.add_model_version
  • B. The machine learning engineer needs to create an entirely new MLflow Model Registry model
  • C. MlflowClient.update_registered_model
  • D. mlflow.register_model
  • E. MlflowClient.get_model_version
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Which of the following describes batch deployment for machine learning projects?

  • A. None of these describe batch deployment for machine learning projects
  • B. Predictions are computed immediately as data arrives and stored for later querying
  • C. Predictions are computed prior to delivery and stored for later querying
  • D. Predictions are computed and delivered as soon as feature values are available
Reveal Solution  Discussion  0

Correct Answer: C  🗳️

Explanation: Only visible for FreeCram members. You can sign-up / login (it's free).

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?

  • A.
  • B.
  • C.
  • D.
  • E.
Reveal Solution  Discussion  0

Correct Answer: B  🗳️

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