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

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

Section

Weight

Objectives

Model Deployment

10%

- Model Serving

1. Custom serving solutions
2. Databricks Model Serving

- Deployment Strategies

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

Model Development

47%

- MLflow Advanced Features

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

- Feature Store

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

- SparkML and Scaling

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

Model Lifecycle Management (MLOps)

43%

- Environment Management

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

- Monitoring

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

- Automated Workflows

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

- Testing Strategies

1. Data quality testing
2. Model testing and validation

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Databricks Certified Machine Learning Professional Sample Questions (Q88-Q93):

NEW QUESTION # 88
What is commonly monitored in deployed ML models?

A. GPU usage only
B. Notebook formatting
C. Code style
D. Model drift and prediction quality

Answer: D

Explanation:
Production ML systems monitor:
data drift
model drift
prediction accuracy.

NEW QUESTION # 89
Which of the following MLflow Model Registry use cases requires the use of an HTTP Webhook?

A. Sending a message to a Slack channel when a model version transitions stages
B. Starting a testing job when a new model is registered
C. Updating data in a source table for a Databricks SQL dashboard when a model version transitions to the Production stage
D. Sending an email alert when an automated testing Job fails
E. None of these use cases require the use of an HTTP Webhook

Answer: C

NEW QUESTION # 90
A Machine Learning Engineer needs to deploy a custom model using Databricks Model Serving.
The model requires an external tokenizer file (for example, a vocabulary or pre-trained tokenizer) to function correctly. They need to ensure this tokenizer file is included with the model so it is available during model serving. How should they package this tokenizer file as part of the model deployment?

A. Use the artifacts parameter in mlflow.pyfunc.log_model() to package the tokenizer file, referencing its path in Volumes.
B. Access the tokenizer file directly from Volumes at model inference time, which requires pre- provisioning access to the Volume for every model serving endpoint.
C. Include tokenizer file as part of the model's software dependencies by adding it to the conda_env specification.
D. Use the code_paths parameter in mlflow.pyfunc.log_model() to include the tokenizer file, assuming it's part of the custom Python logic needed by the model.

Answer: A

Explanation:
The artifacts parameter in mlflow.pyfunc.log_model is designed for packaging non-code assets required at inference time, such as tokenizer files. By logging the tokenizer as a model artifact and referencing its path, MLflow ensures the file is versioned with the model and automatically made available to Databricks Model Serving during inference.

NEW QUESTION # 91
Which of the following is a simple statistic to monitor for categorical feature drift?

A. Percentage of missing values
B. None of these
C. Number of unique values
D. Mode
E. Mode, number of unique values, and percentage of missing values

Answer: E

NEW QUESTION # 92
A Machine Learning Engineer uses Lakehouse Monitoring to track their credit scoring model's performance. The existing profile metrics table contains three aggregate metrics:
- adefault_risk_score
- payment_history_score
- credit_utilization_score
They need to:
1. Create a composite risk rating that combines these three scores using weights of 0.5, 0.3, and 0.2 respectively.
2. Monitor drift of this composite score against an established baseline.
Which approach should be used to implement both requirements within Lakehouse Monitoring?

A. Create two separate aggregate metrics: one for the composite score calculation and another for drift detection.
B. Use a scheduled notebook to calculate the composite score and manually insert both the score and drift values into the profile metrics table.
C. Create an aggregate metric for composite_risk_rating and configure a separate drift metric to monitor changes.
D. Create a derived metric for the composite_risk_rating calculation and create a drift metric on this derived metric.

Answer: D

Explanation:
Lakehouse Monitoring supports derived metrics that are computed from existing profile metrics using custom expressions. By defining a derived metric for the composite_risk_rating using the specified weights, the composite score becomes a first-class metric in the monitoring framework.
A drift metric can then be directly configured on this derived metric to compare current values against the baseline, fulfilling both the composite calculation and drift monitoring requirements in a native, governed way.

NEW QUESTION # 93
......

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