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

Section	Weight	Objectives
Model Lifecycle Management (MLOps)	43%	- Monitoring 1. Model performance monitoring 2. Lakehouse Monitoring for drift detection - Environment Management 1. Databricks Asset Bundles (DABs) 2. Environment configuration and reproducibility - Automated Workflows 1. Automated retraining workflows 2. CI/CD pipelines for ML - Testing Strategies 1. Data quality testing 2. Model testing and validation
Model Development	47%	- Feature Store 1. Feature Store concepts and usage 2. Automated feature pipelines - SparkML and Scaling 1. Distributed training and hyperparameter tuning 2. Ray/Optuna distributed tuning 3. Building scalable ML pipelines with SparkML - MLflow Advanced Features 1. Experiment tracking and model management 2. Model registry and versioning
Model Deployment	10%	- Deployment Strategies 1. Real-time Deployment 2. Streaming Deployment 3. Batch Deployment - Model Serving 1. Databricks Model Serving 2. Custom serving solutions

Section

Weight

Objectives

Model Lifecycle Management (MLOps)

43%

- Monitoring

1. Model performance monitoring
2. Lakehouse Monitoring for drift detection

- Environment Management

1. Databricks Asset Bundles (DABs)
2. Environment configuration and reproducibility

- Automated Workflows

1. Automated retraining workflows
2. CI/CD pipelines for ML

- Testing Strategies

1. Data quality testing
2. Model testing and validation

Model Development

47%

- Feature Store

1. Feature Store concepts and usage
2. Automated feature pipelines

- SparkML and Scaling

1. Distributed training and hyperparameter tuning
2. Ray/Optuna distributed tuning
3. Building scalable ML pipelines with SparkML

- MLflow Advanced Features

1. Experiment tracking and model management
2. Model registry and versioning

Model Deployment

10%

- Deployment Strategies

1. Real-time Deployment
2. Streaming Deployment
3. Batch Deployment

- Model Serving

1. Databricks Model Serving
2. Custom serving solutions

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Databricks-Machine-Learning-Professional Zertifikatsdemo - Databricks-Machine-Learning-Professional Online Test

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Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional Prüfungsfragen mit Lösungen (Q122-Q127):

122. Frage
A Machine Learning Engineer has automated a model retraining job in Databricks. Each scheduled run trains multiple candidate models with new sales data and logs all runs with MLflow.
The goal is to select and register the best-performing model at the end of each cycle to ensure optimal forecast accuracy. Which approach will meet this goal?

A. Register the best model based on the primary evaluation metric.
B. Register the first model that has an evaluation metric better than the previously deployed model.
C. Register the model with the lowest training loss regardless of validation metrics.
D. Register all models from the run since all of them are trained on newer data and hence will be more accurate.

Antwort: A

Begründung:
Selecting and registering the model that performs best on the primary evaluation metric ensures that only the highest-quality model is promoted at each retraining cycle. This approach aligns with MLOps best practices by basing promotion decisions on objective performance criteria rather than training order or assumptions about data freshness.

123. Frage
A machine learning engineer wants to log feature importance data from a CSV file at path importance_path with an MLflow run for model model.
Which of the following code blocks will accomplish this task inside of an existing MLflow run block?
A)

B)

C) mlflow.log_data(importance_path, "feature-importance.csv")
D) mlflow.log_artifact(importance_path, "feature-importance.csv")
E) None of these code blocks tan accomplish the task.

A. Option B
B. Option D
C. Option A
D. Option C
E. Option E

Antwort: C

124. Frage
Which of the following describes label drift?

A. None of these describe label drift
B. Label drift is when there is a change in the distribution of an input variable
C. Label drift is when there is a change in the distribution of a target variable
D. Label drift is when there is a change in the distribution of the predicted target given by the model
E. Label drift is when there is a change in the relationship between input variables and target variables

Antwort: B

125. Frage
Which of the following is a simple, low-cost method of monitoring numeric feature drift?

A. Summary statistics trends
B. None of these can be used to monitor feature drift
C. Chi-squared test
D. Jensen-Shannon test
E. Kolmogorov-Smirnov (KS) test

Antwort: A

126. Frage
A Machine Learning Engineer has previously built a feature table for model training and inference using a batch mode approach:

They have been informed that they now require these features to be available in "real-time", with latency on the order of a minute. Their manager has informed them there is now a Kafka stream from which they can stream live data, and they need to have this ingested and available for low- latency feature lookups.
Which change to their existing code will achieve this?

A. Create a custom pyfunc MLflow model which processes results of the Kafka stream for on demand feature calculation.
B. Run a triggered workflow to ingest the Kafka data to a dataframe that they can use with their existing write_table() command.
C. Change the incoming_df to be a dataframe based on a readStream() from the kafka source, the write_table() method will provide a low-latency lookup on this data.
D. Change the incoming_df to be a dataframe based on a readStream() from the Kafka source and publish the table as an online table with the streaming option set to True.

Antwort: D

Begründung:
To achieve real-time availability with minute-level latency, the feature data must be continuously ingested from Kafka and published to an online table. Using a streaming DataFrame created with readStream from the Kafka source and enabling the online table with streaming allows incremental updates to be synchronized to the online store, supporting low-latency feature lookups for real-time inference.

127. Frage
......

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