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

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

Section

Weight

Objectives

Topic 1: Model Development

47%

- Feature Store

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

- MLflow Advanced Features

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

- SparkML and Scaling

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

Topic 2: Model Deployment

10%

- Model Serving

1. Databricks Model Serving
2. Custom serving solutions

- Deployment Strategies

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

Topic 3: Model Lifecycle Management (MLOps)

43%

- Testing Strategies

1. Data quality testing
2. Model testing and validation

- Monitoring

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

- Automated Workflows

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

- Environment Management

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

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Databricks Certified Machine Learning Professional Databricks-Machine-Learning-Professional Prüfungsfragen mit Lösungen (Q74-Q79):

74. Frage
A Machine Learning Engineer is conducting hyperparameter tuning for multiple XGBoost models using Ray Tune on Databricks. They want to integrate MLflow tracking to monitor their experiments and need to ensure proper authentication. The engineer has Ray 2.41 installed and wants to use both Ray Tune and MLflow together in their distributed tuning workflow. They have to configure Databricks to run the hyperparameter optimization with MLflow integration. Which set of configuration steps will do this?

A. Enable MLflow autologging with mlflow.ray.autolog() and set the tracking server URI.
B. Install the MLflow Ray plugin using %pip install mlflow-ray and configure the workspace connection.
C. Set MLFLOW_TRACKING_URI and MLFLOW_EXPERIMENT_TD environment variables before initializing Ray.
D. Configure DATABRICKS_HOST and DATABRICKS_TOKEN environment variables before calling setup_ray_cluster().

Antwort: D

Begründung:
When using Ray Tune with MLflow on Databricks, Ray workers must be able to authenticate back to the Databricks workspace to log runs to MLflow Tracking. Setting the DATABRICKS_HOST and DATABRICKS_TOKEN environment variables before initializing the Ray cluster ensures all Ray processes can securely communicate with Databricks and correctly log MLflow experiments during distributed hyperparameter tuning.

75. Frage
A machine learning engineer has registered a sklearn model in the MLflow Model Registry using the sklearn model flavor with UI model_uri. Which operation can be used to load the model as an sklearn object for batch deployment?

A. mlflow.pyfunc.read_model(model_uri)
B. mlflow.sklearn.load_model(model_uri)
C. mlflow.pyfunc.load_model(model_uri)
D. mlflow.spark.load_model(model_uri)
E. mlflow.sklearn.read_model(model_uri)

Antwort: C

76. Frage
A data scientist set up a machine learning pipeline to automatically log a data visualization with each run. They now want to view the visualizations in Databricks.
Which of the following locations in Databricks will show these data visualizations?

A. The MLflow Model Registry Model paqe
B. The Artifacts section of the MLflow Experiment page
C. The Artifacts section of the MLflow Run page
D. The Figures section of the MLflow Run page
E. Logged data visualizations cannot be viewed in Databricks

Antwort: D

77. Frage
A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?

A. Add the nested=True argument to the parent run
B. Provide the same name to the run name parameter for all three run blocks
C. Indent the child run blocks within the parent run block
D. Remove the nested=True argument from the child runs
E. Add the nested=True argument to the parent run and remove the nested=True arguments from the child runs

Antwort: E

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

Antwort: B

Begründung:
When a data scientist logs feature importance data (e.g., as a CSV or JSON file) using MLflow, it is stored as an artifact of that specific run. These artifacts are visible under the Artifacts section of the MLflow Run page in Databricks, where users can view or download the logged files associated with that particular experiment run.

79. Frage
......

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