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

Section	Weight	Objectives
Topic 1: Model Lifecycle Management (MLOps)	43%	- Testing Strategies 1. Model testing and validation 2. Data quality testing - 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. Lakehouse Monitoring for drift detection 2. Model performance monitoring
Topic 2: 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
Topic 3: Model Development	47%	- MLflow Advanced Features 1. Experiment tracking and model management 2. Model registry and versioning - SparkML and Scaling 1. Distributed training and hyperparameter tuning 2. Ray/Optuna distributed tuning 3. Building scalable ML pipelines with SparkML - Feature Store 1. Automated feature pipelines 2. Feature Store concepts and usage

Section

Weight

Objectives

Topic 1: Model Lifecycle Management (MLOps)

43%

- Testing Strategies

1. Model testing and validation
2. Data quality testing

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

Topic 2: 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

Topic 3: Model Development

47%

- MLflow Advanced Features

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

- SparkML and Scaling

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

- Feature Store

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

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Databricks Certified Machine Learning Professional Sample Questions (Q144-Q149):

NEW QUESTION # 144
Which MLflow operation can be used to log a plot that was generated during an MLflow run?

A. mlflow.log_visualization
B. mlflow.log_figure
C. None of these operations can accomplish the task.
D. mlflow.log_metric

Answer: B

Explanation:
The mlflow.log_figure() operation is used to log plots or figures generated during an MLflow run. It takes a Matplotlib, Plotly, or similar figure object and saves it as an artifact in the MLflow tracking server, enabling visualization and later retrieval from the MLflow UI.

NEW QUESTION # 145
After a data scientist noticed that a column was missing from a production feature set stored as a Delta table, the machine learning engineering team has been tasked with determining when the column was dropped from the feature set.
Which of the following SQL commands can be used to accomplish this task?

A. HISTORY
B. TIMESTAMP
C. VERSION
D. DESCRIBE HISTORY
E. DESCRIBE

Answer: D

NEW QUESTION # 146
A data scientist wants to examine the data in the Feature Store table table from the database dev as a Spark DataFrame. They have access to Feature Store Client fs. Which line of code can be used to gel the data from table as a Spark DataFrame?

A. fs.get_table("dev.table")
B. fs.get_table("table)
C. fs.read_table("dev.table")
D. fs.create_table("dev.table")

Answer: C

Explanation:
To read data from a Feature Store table as a Spark DataFrame, the correct method is fs.read_table("dev.table"). This retrieves the contents of the table in the dev database using the Feature Store Client (fs) and returns it as a Spark DataFrame.

NEW QUESTION # 147
A machine learning engineer wants to programmatically create a new Databricks Job whose schedule depends on the result of some automated tests in a machine learning pipeline. Which Databricks tool can be used to programmatically create the Job?

A. MLflow APIs
B. Databricks REST APIs
C. AutoML APIs
D. Jobs cannot be created programmatically
E. MLflow Client

Answer: B

NEW QUESTION # 148
A machine learning engineer is attempting to create a webhook that will trigger a Databricks Job job_id when a model version for model model transitions into any MLflow Model Registry stage.
They have the following incomplete code block:

Which of the following lines of code can be used to fill in the blank so that the code block accomplishes the task?

A. "MODEL_VERSION_CREATED"
B. "MODEL_VERSION_TRANSITIONED_STAGE"
C. "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"
D. "MODEL_VERSION_TRANSITIONED_TO_STAGING"
E. "MODEL_VERSION_TRANSITIONED_TO_STAGING", "MODEL_VERSION_TRANSITIONED_TO_PRODUCTION"

Answer: D

NEW QUESTION # 149
......

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