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

Section	Weight	Objectives
Model Deployment	10%	- Model Serving 1. Databricks Model Serving 2. Custom serving solutions - Deployment Strategies 1. Streaming Deployment 2. Real-time Deployment 3. Batch Deployment
Model Development	47%	- Feature Store 1. Feature Store concepts and usage 2. Automated feature pipelines - 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. 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. Model testing and validation 2. Data quality testing

Section

Weight

Objectives

Model Deployment

10%

- Model Serving

1. Databricks Model Serving
2. Custom serving solutions

- Deployment Strategies

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

Model Development

47%

- Feature Store

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

- 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. 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. Model testing and validation
2. Data quality testing

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Databricks Certified Machine Learning Professional Sample Questions (Q130-Q135):

NEW QUESTION # 130
A machine learning engineer needs to select a deployment strategy for a new machine learning application. The feature values are not available until the time of delivery, and results are needed exceedingly fast for one record at a time.
Which of the following deployment strategies can be used to meet these requirements?

A. Real-time
B. Batch
C. Streaming
D. Edge/on-device
E. None of these strategies will meet the requirements.

Answer: A

NEW QUESTION # 131
A Machine Learning Engineer has deployed a fraud detection model in Databricks Model Serving to detect fraudulent transactions. The engineer wants to compare the model's predictions with the actual fraud classifications from the Fraud Ops team to monitor model performance. The Fraud Ops team uses a unique transaction_id to investigate fraudulent activity and persist their findings to a fraud_findings table. The engineer enabled inference tables on the endpoint, but they are not sure how to map the models' predictions to the Fraud Ops team's classifications. How can the engineer uniquely join the models' prediction to the fraud_findings table with the fewest code changes?

A. Store databricks_request_id returned from each model serving request and persist it to the fraud_findings table. Join the inference table with the fraud_findings table using databricks_request_id as the join key.
B. Join the inference table with the fraud_findings table using timestamp_ms as the join key.
C. Populate the client_request_id field with the transaction_id in the model serving request body.
Join the inference table with the fraud_findings table using client_request_id (which contains the transaction_id) as the join key.
D. Modify the model to include an additional input: transaction_id. Log, register and deploy the new model. In the model serving request body, add transaction_id as an additional input feature. Join the inference table with the fraud_findings table using transaction_id as the join key.

Answer: C

Explanation:
Databricks Model Serving inference tables automatically log the client_request_id field for each request. By populating this field with the existing transaction_id in the request body, the engineer can directly and uniquely join inference predictions with the fraud_findings table using the same identifier, achieving accurate performance monitoring with minimal code changes and no model retraining or redeployment.

NEW QUESTION # 132
Why are Delta tables often used to store machine learning features?

A. They reduce model size
B. They support schema enforcement and time travel
C. They replace Spark DataFrames
D. They allow faster GPU training

Answer: B

Explanation:
Delta Lake provides:
ACID transactions
time travel
schema enforcement
These are essential for reproducible ML pipelines.

NEW QUESTION # 133
Which MLflow command logs a trained model?

A. mlflow.run()
B. mlflow.start_run()
C. mlflow.register_model()
D. mlflow.log_model()

Answer: D

Explanation:
mlflow.log_model() saves the model artifact within the run.

NEW QUESTION # 134
Which of the following tools can assist in real-time deployments by packaging software with its own application, tools, and libraries?

A. None of these tools
B. Autoscaling clusters
C. Containers
D. Cloud-based compute
E. REST APIs

Answer: D

NEW QUESTION # 135
......

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