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

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

Section

Weight

Objectives

Model Lifecycle Management (MLOps)

43%

- Automated Workflows

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

- Environment Management

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

- Monitoring

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

- Testing Strategies

1. Model testing and validation
2. Data quality testing

Model Deployment

10%

- Model Serving

1. Databricks Model Serving
2. Custom serving solutions

- Deployment Strategies

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

Model Development

47%

- SparkML and Scaling

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

- 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

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Databricks Certified Machine Learning Professional Sample Questions (Q143-Q148):

NEW QUESTION # 143
What tool helps avoid feature mismatch between training and inference?

A. Model Registry
B. Feature Store
C. MLflow Projects
D. Spark SQL

Answer: B

Explanation:
Feature Store ensures:
same feature computation logic
consistency between training and serving.

NEW QUESTION # 144
Which of the following describes the concept of MLflow Model flavors?

A. A convention that MLflow Model Registry can use to organize its Models by project
B. A convention that deployment tools can use to wrap preprocessing logic into a Model
C. A convention that deployment tools can use to understand the model
D. A convention that MLflow Model Registry can use to version models
E. A convention that MLflow Experiments can use to organize their Runs by project

Answer: E

NEW QUESTION # 145
A data scientist has developed a scikit-learn model sklearn_model and they want to log the model using MLflow.
They write the following incomplete code block:

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

A. mlflow.sklearn.log_model(sklearn_model, "model")
B. mlflow.spark.track_model(sklearn_model, "model")
C. mlflow.spark.log_model(sklearn_model, "model")
D. mlflow.sklearn.track_model(sklearn_model, "model")
E. mlflow.sklearn.load_model("model")

Answer: B

NEW QUESTION # 146
A Machine Learning Engineer is tasked with building an automated daily pipeline that updates a customer_features table in Unity Catalog. They have implemented a function, compute_customer_features, that returns a DataFrame with a unique customer_id as the primary key and want to ensure the latest feature values are merged into the table each day. Which code snippet implements this requirement?

Answer: B

Explanation:
Using write_table with mode set to merge updates existing rows and inserts new ones based on the table's primary key. This ensures that the latest feature values for each customer_id are merged into the existing feature table each day without overwriting the entire table, which is the correct and scalable approach for maintaining up-to-date customer features in Unity Catalog.

NEW QUESTION # 147
A machine learning engineer is using the following code block as part of a batch deployment pipeline:

Which of the following changes needs to be made so this code block will work when the inference table is a stream source?

A. Replace spark.read with spark.readStream
B. Replace predict with a stream-friendly prediction function
C. Replace schema(schema) with option("maxFilesPerTriqqer", 1}
D. Replace formatfdelta") with format("stream")
E. Replace "inference" with the path to the location of the Delta table

Answer: C

NEW QUESTION # 148
......

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