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

Section	Weight	Objectives
Model Deployment	10%	- Deployment Strategies 1. Batch Deployment 2. Real-time Deployment 3. Streaming Deployment - Model Serving 1. Databricks Model Serving 2. Custom serving solutions
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. Ray/Optuna distributed tuning 2. Distributed training and hyperparameter tuning 3. Building scalable ML pipelines with SparkML
Model Lifecycle Management (MLOps)	43%	- Environment Management 1. Environment configuration and reproducibility 2. Databricks Asset Bundles (DABs) - Automated Workflows 1. CI/CD pipelines for ML 2. Automated retraining workflows - Monitoring 1. Model performance monitoring 2. Lakehouse Monitoring for drift detection - Testing Strategies 1. Model testing and validation 2. Data quality testing

Section

Weight

Objectives

Model Deployment

10%

- Deployment Strategies

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

- Model Serving

1. Databricks Model Serving
2. Custom serving solutions

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

Model Lifecycle Management (MLOps)

43%

- Environment Management

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

- Automated Workflows

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

- Monitoring

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

- Testing Strategies

1. Model testing and validation
2. Data quality testing

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Databricks Certified Machine Learning Professional Sample Questions (Q163-Q168):

NEW QUESTION # 163
Which of the following lists all of the model stages are available in the MLflow Model Registry?

A. Staging. Production. Archived
B. Development. Staging. Production. Archived
C. None. Staging. Production
D. None. Staging. Production. Archived
E. Development. Staging. Production

Answer: E

NEW QUESTION # 164
A Machine Learning Engineer has trained a credit scoring model and needs to evaluate fairness metrics across different customer segments while maintaining different levels of granularity for business reporting. They need to compute metrics like precision, recall, and demographic parity at the individual feature level (credit_score_range, income_bracket) as well as intersectional slices (combinations of features). The model outputs are stored in a Delta table with prediction probabilities and actual default labels. The engineer wants to systematically evaluate model performance across these various feature slices and granularities. Which approach will do this?

A. Create Lakehouse Monitoring with slicing expressions for individual features and intersection conditions.
B. Implement custom MLflow evaluation functions that iterate through all possible feature combinations.
C. Build separate Spark DataFrames for each slice and compute metrics using standard DataFrame operations.
D. Use Unity Catalog metric views with dimensions defined for each feature and measures for the fairness metrics.

Answer: A

Explanation:
Lakehouse Monitoring supports defining slicing expressions on one or more columns, allowing metrics such as precision, recall, and fairness indicators to be computed at both individual feature levels and intersectional combinations. This provides a systematic, scalable, and governed way to evaluate model performance and fairness across multiple granularities directly from Delta tables without custom metric pipelines.

NEW QUESTION # 165
A machine learning engineering team has written predictions computed in a batch job to a Delta table for querying. However, the team has noticed that the querying is running slowly. The team has already tuned the size of the data files. Upon investigating, the team has concluded that the rows meeting the query condition are sparsely located throughout each of the data files. Based on the scenario, which optimization technique could speed up the query by colocating similar records while considering values in multiple columns?

A. Data skipping
B. Z-Ordering
C. Tuning the file size
D. Bin-packing
E. Write as a Parquet file

Answer: B

NEW QUESTION # 166
A data scientist has written a function to track the runs of their random forest model. The data scientist is changing the number of trees in the forest across each run. Which of the following MLflow operations is designed to log single values like the number of trees in a random forest?

A. mlflow.log_artifact
B. mlflow.log_metric
C. There is no way to store values like this.
D. mlflow.log_model
E. mlflow.log_param

Answer: B

NEW QUESTION # 167
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model.
Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

A. This approach has no impact when loading the logged Pvfunc model for downstream deployment
B. There is no longer a need for pipeline-like machine learning objects
C. The same preprocessing logic will automatically be applied when calling fit
D. The pvfunc model can be used to deploy models in a parallelizable fashion
E. The same preprocessing logic will automatically be applied when calling predict

Answer: B

NEW QUESTION # 168
......

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