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

Topic	Details
Topic 1	Identify the requirements for tracking nested runs Describe an MLflow flavor and the benefits of using MLflow flavors
Topic 2	Describe concept drift and its impact on model efficacy Describe summary statistic monitoring as a simple solution for numeric feature drift
Topic 3	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 4	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 5	Identify live serving benefits of querying precomputed batch predictions Describe Structured Streaming as a common processing tool for ETL pipelines
Topic 6	Describe model serving deploys and endpoint for every stage Identify scenarios in which feature drift and or label drift are likely to occur
Topic 7	Describe the advantages of using the pyfunc MLflow flavor Manually log parameters, models, and evaluation metrics using MLflow
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

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

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Databricks Certified Machine Learning Professional Sample Questions:

Question #1

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

Reveal Solution Discussion 0

Correct Answer: A 🗳️

Explanation: Only visible for Test4Engine members. You can sign-up / login (it's free).

Question #2

A Data Scientist is developing a model training pipeline on Databricks and needs to track custom performance metrics during training. They want to log a custom evaluation score (team_score), a single hyperparameter, and a confusion matrix plot as part of their MLflow experiment. Which code snippet correctly logs all three types of information in MLflow?

A. mlflow.log_param("max_depth", 5)
mlflow.log_metric("team_score", 0.92)
mlflow.log_artifact("confusion_matrix.png")
B. mlflow.log_metric({"team_score": 0.92})
mlflow.log_param(["max_depth", 5])
mlflow.log_file("confusion_matrix.png")
C. mlflow.log_param("max_depth", 5)
mlflow.log_metric("team_score", 0.92)
mlflow.log_artifact(open("confusion_matrix.png"))
D. mlflow.log_param("max_depth", "5")
mlflow.log_metric("team_score", "0.92")
mlflow.log_artifact("confusion_matrix.png")

Reveal Solution Discussion 0

Correct Answer: A 🗳️

Explanation: Only visible for Test4Engine members. You can sign-up / login (it's free).

Question #3

In a Spark ML Pipeline, what is the role of a Transformer?

A. Fits data
B. Trains models
C. Evaluates models
D. Converts datasets into new datasets

Reveal Solution Discussion 0

Correct Answer: D 🗳️

Explanation: Only visible for Test4Engine members. You can sign-up / login (it's free).

Question #4

A machine learning engineer has deployed a model recommender using MLflow Model Serving.
They now want to query the version of that model that is in the Production stage of the MLflow Model Registry. Which of the following model URIs can be used to query the described model version?

A. The version number of the model version in Production is necessary to complete this task.
B. https://<databricks-instance>/model-serving/recommender/stage-production/invocations
C. https://<databricks-instance>/model/recommender/Production/invocations
D. https://<databricks-instance>/model-serving/recommender/Production/invocations
E. https://<databricks-instance>/model/recommender/stage-production/invocations