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

Section	Weight	Objectives
Topic 1: Model Deployment	10%	- Deployment Strategies 1. Real-time Deployment 2. Batch Deployment 3. Streaming Deployment - Model Serving 1. Custom serving solutions 2. Databricks Model Serving
Topic 2: Model Lifecycle Management (MLOps)	43%	- Environment Management 1. Databricks Asset Bundles (DABs) 2. Environment configuration and reproducibility - 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
Topic 3: Model Development	47%	- MLflow Advanced Features 1. Experiment tracking and model management 2. Model registry and versioning - 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

Section

Weight

Objectives

Topic 1: Model Deployment

10%

- Deployment Strategies

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

- Model Serving

1. Custom serving solutions
2. Databricks Model Serving

Topic 2: Model Lifecycle Management (MLOps)

43%

- Environment Management

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

- 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

Topic 3: Model Development

47%

- MLflow Advanced Features

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

- 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

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Databricks Certified Machine Learning Professional Sample Questions (Q147-Q152):

NEW QUESTION # 147
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 SQL command can be used to accomplish this task?

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

Answer: C

NEW QUESTION # 148
A machine learning engineer is in the process of implementing a concept drift monitoring solution.
They are planning to use the following steps:
1. Deploy a model to production and compute predicted values
2. Obtain the observed (actual) label values
3. _____
4. Run a statistical test to determine if there are changes over time
Which of the following should be completed as Step #3?

A. Retrain the model
B. Measure the latency of the prediction time
C. Obtain the observed values (actual) feature values
D. None of these should be completed as Step #3
E. Compute the evaluation metric using the observed and predicted values

Answer: D

NEW QUESTION # 149
A Data Scientist is building a propensity model for an e-commerce start-up. The company maintains 7GB of historical data and receives about 5MB of new transaction data daily. The goal is to generate daily purchase predictions for all users by 7:00 AM each morning. As the start-up is in its early stages, the data scientist must prioritize a highly cost-efficient approach. Which approach should the Data Scientist take?

A. Use a single-node memory compute to build a model with libraries like scikit-learn. The model can then be scheduled as a nightly batch job that runs on a single-node compute.
B. Use a single-node compute to build a model with libraries like scikit-learn. The model can then be deployed as an always-on REST API so that users can query the API to get the predictions whenever they want.
C. Use a single-node compute to build a model with libraries like scikit-learn. The model can then be integrated into an always-on streaming pipeline, to ensure immediate processing of incoming data in order to meet the SLA.
D. Use a multi-node compute and leverage distributed frameworks like SparkML to train the model.
The model can then be scheduled as a nightly batch job that runs on a multi-node compute.

Answer: A

Explanation:
With only 7GB of historical data and a small daily increment (about 5MB), a single-node memory- optimized cluster can comfortably train and score using scikit-learn without the overhead and cost of distributed compute. Scheduling a nightly batch job is the most cost-efficient way to meet a fixed daily SLA (7:00 AM) because the compute can be started only for the job run and then terminated, avoiding the expense of always-on serving or streaming infrastructure.

NEW QUESTION # 150
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. Tuning the file size
B. Bin-packing
C. Write as a Parquet file
D. Data skipping
E. Z-Ordering

Answer: E

NEW QUESTION # 151
A Machine Learning Engineer is considering moving their functions and unit tests from notebooks into separate Python files (e.g., modules and test scripts) to take advantage of the numerous benefits of this approach like automated execution, code reusability, and version control. Which challenge should the engineer consider with this approach?

A. This change would prevent the use of any non-Python environments like Scala.
B. Managing a more complex project structure can be harder to maintain and navigate.
C. It makes functions harder to import and reuse across different notebooks.
D. Separating code and tests often leads to decreased reliability and poor code quality.

Answer: B

Explanation:
Moving code and unit tests into separate Python modules introduces a more structured project layout, which can increase complexity. Engineers must manage directories, dependencies, and imports carefully, making the project slightly harder to navigate and maintain compared to simple notebook-based workflows, especially for teams new to this structure.

NEW QUESTION # 152
......

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