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

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

Section

Weight

Objectives

Topic 1: Model Development

47%

- SparkML and Scaling

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

- Feature Store

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

- MLflow Advanced Features

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

Topic 2: Model Lifecycle Management (MLOps)

43%

- Automated Workflows

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

- Testing Strategies

1. Model testing and validation
2. Data quality testing

- Environment Management

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

- Monitoring

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

Topic 3: Model Deployment

10%

- Deployment Strategies

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

- Model Serving

1. Databricks Model Serving
2. Custom serving solutions

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Databricks Certified Machine Learning Professional Sample Questions (Q18-Q23):

NEW QUESTION # 18
Which of the following describes the purpose of the context parameter in the predict method of Python models for MLflow?

A. The context parameter allows the user to provide the model access to objects like preprocessing models or custom configuration files
B. The context parameter allows the user to include relevant details of the business case to allow downstream users to understand the purpose of the model
C. The context parameter allows the user to document the performance of a model after it has been deployed
D. The context parameter allows the user to specify which version of the registered MLflow Model should be used based on the given application's current scenario
E. The context parameter allows the user to provide the model with completely custom if-else logic for the given application's current scenario

Answer: D

NEW QUESTION # 19
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_model
B. mlflow.log_metric
C. There is no way to store values like this.
D. mlflow.log_artifact
E. mlflow.log_param

Answer: B

NEW QUESTION # 20
A Machine Learning Engineer wants to implement MLOps. They currently have three environments: dev, stage, and prod. They have many private PyPI packages that they use to train their models, and they are concerned that their environments will not be consistent between dev, stage, and prod. The engineer needs to follow enterprise scaling and CI/CD best practices to ensure that their environments are consistent. Which approach will do this?

A. Use the same DBR for ML runtime across environments and %pip install any additional packages at the top of the notebook.
B. Define an all purpose cluster with the private PyPI packages installed, and use the same cluster config across environments.
C. Use the same DBR across environments.
D. Define dependencies for each task in their job(s) running the ML pipeline using Databricks Asset Bundles or Terraform.

Answer: D

Explanation:
Defining dependencies at the job or task level using Databricks Asset Bundles or Terraform aligns with enterprise MLOps and CI/CD best practices. This approach makes environment configuration declarative, version-controlled, and reproducible across dev, stage, and prod, ensuring consistent use of private PyPI packages without relying on mutable clusters or manual installation steps.

NEW QUESTION # 21
Which tool can be used to automatically start a testing Job when a new version of an MLflow Model Registry model is registered?

A. MLflow Model Registry Webhooks
B. MLflow REST API
C. MLflow Client API
D. MLflow Model Registry UI

Answer: A

Explanation:
MLflow Model Registry Webhooks can be configured to automatically trigger actions - such as running a testing job - when events occur, like registering a new model version. This enables automation in CI/CD workflows for machine learning models.

NEW QUESTION # 22
A machine learning engineer has registered a Spark ML model in the MLflow Model Registry using the Spark ML model flavor with UI model_uri. Which operation can be used to load the model as a Spark ML object for batch deployment?

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

Answer: B

Explanation:
To load a model that was logged using the Spark ML flavor, the correct operation is mlflow.spark.load_model(model_uri). This restores the model as a Spark ML object, preserving its original structure and functionality for batch inference or further processing in Spark environments.

NEW QUESTION # 23
......

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