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

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

Section

Weight

Objectives

Topic 1: Model Development

47%

- Feature Store

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

- MLflow Advanced Features

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

- SparkML and Scaling

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

Topic 2: Model Lifecycle Management (MLOps)

43%

- Automated Workflows

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

- Monitoring

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

- Environment Management

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

- Testing Strategies

1. Data quality testing
2. Model testing and validation

Topic 3: Model Deployment

10%

- Model Serving

1. Custom serving solutions
2. Databricks Model Serving

- Deployment Strategies

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

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Databricks Certified Machine Learning Professional Sample Questions (Q86-Q91):

NEW QUESTION # 86
A machine learning engineer has deployed a model recommender using MLflow Model Serving.
They now want to query the version of that model that is in the Staging stage of the MLflow Model Registry. Which model URI can be used to query the described model version?

A. The version number of the model version in Production is necessary to complete this task.
B. https://<databricks-instance>/model/recommender/Staging/invocations
C. It is not possible to query a model version in the Staging stage.
D. https://<databricks-instance>/model-serving/recommender/Staging/invocations

Answer: B

Explanation:
To query a model version in a specific stage (like Staging) using MLflow Model Serving on Databricks, the correct model URI format is:
https://<databricks-instance>/model/<model-name>/<stage>/invocations
So, for the model named recommender in the Staging stage, the correct URI is:
https://<databricks-instance>/model/recommender/Staging/invocations.

NEW QUESTION # 87
Which of the following statements describes streaming with Spark as a model deployment strategy?

A. The inference of batch processed records as soon as a trigger is hit
B. The inference of batch processed records as soon as a Spark job is run
C. The inference of incrementally processed records as soon as trigger is hit
D. The inference of all types of records in real-time
E. The inference of incrementally processed records as soon as a Spark job is run

Answer: E

NEW QUESTION # 88
A data scientist has developed a scikit-learn random forest model model, but they have not yet logged model with MLflow. They want to obtain the input schema and the output schema of the model so they can document what type of data is expected as input.
Which of the following MLflow operations can be used to perform this task?

A. mlflow.models.Model.get_input_schema
B. There is no way to obtain the input schema and the output schema of an unlogged model.
C. mlflow.models.Model.signature
D. mlflow.models.schema.infer_schema
E. mlflow.models.signature.infer_signature

Answer: D

NEW QUESTION # 89
A data scientist has developed and logged a scikit-learn random forest model model, and then they ended their Spark session and terminated their cluster. After starting a new cluster, they want to review the feature_importances_ of the original model object.
Which of the following lines of code can be used to restore the model object so that feature_importances_ is available?

A. client.list_artifacts(run_id)["feature-importances.csv"]
B. mlflow.sklearn.load_model(model_uri)
C. client.pyfunc.load_model(model_uri)
D. mlflow.load_model(model_uri)
E. This can only be viewed in the MLflow Experiments UI

Answer: D

NEW QUESTION # 90
Which of the following lists all of the model stages are available in the MLflow Model Registry?

A. Staging. Production. Archived
B. Development. Staging. Production. Archived
C. Development. Staging. Production
D. None. Staging. Production. Archived
E. None. Staging. Production

Answer: D

NEW QUESTION # 91
......

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