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

SectionWeightObjectives
Topic 1: Model Deployment10%- Model Serving
  • 1. Custom serving solutions
  • 2. Databricks Model Serving
- Deployment Strategies
  • 1. Batch Deployment
  • 2. Real-time Deployment
  • 3. Streaming Deployment
Topic 2: Model Development47%- 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 3: Model Lifecycle Management (MLOps)43%- Monitoring
  • 1. Model performance monitoring
  • 2. Lakehouse Monitoring for drift detection
- Automated Workflows
  • 1. CI/CD pipelines for ML
  • 2. Automated retraining workflows
- Environment Management
  • 1. Environment configuration and reproducibility
  • 2. Databricks Asset Bundles (DABs)
- Testing Strategies
  • 1. Data quality testing
  • 2. Model testing and validation

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Databricks Certified Machine Learning Professional Sample Questions (Q182-Q187):

NEW QUESTION # 182
A machine learning engineer wants to move their model version model_version for the MLflow Model Registry model model from the Staging stage to the Production stage using MLflow Client client. Which code block can they use to accomplish the task?

Answer: B

Explanation:
The correct method for transitioning a model version to a new stage using the MLflow Client is client.transition_model_version_stage(name, version, stage). This updates the stage of a specific model version in the Model Registry, such as moving it from "Staging" to "Production". The stage parameter specifies the target stage only -- there is no from argument.


NEW QUESTION # 183
A Machine Learning Engineer has a production AI model that predicts fraudulent transactions in real time. This model is integrated into critical business workflows and any downtime could result in significant financial loss and regulatory penalties. The engineer needs to deploy a new, improved version of the model to production to replace the existing model. The bank's fairness policy requires that all transactions be exposed to the same model at any given time. Additionally, the deployment must meet the following requirements:
- Zero downtime: The fraud detection service must remain continuously
available to users and downstream systems.
- Immediate rollback: If the new model causes issues, you must be able
to revert to the previous version instantly.
Which deployment strategy meets these requirements?

Answer: B

Explanation:
Blue-green deployment maintains two fully operational production environments and switches all traffic from the old model to the new model at once. This guarantees zero downtime and ensures that all transactions are handled by the same model version at any given time. If issues arise, traffic can be immediately switched back to the previous environment, enabling instant rollback while meeting strict fairness and availability requirements.


NEW QUESTION # 184
A machine learning engineer wants to log and deploy a model as an MLflow pyfunc model. They have custom preprocessing that needs to be completed on feature variables prior to fitting the model or computing predictions using that model. They decide to wrap this preprocessing in a custom model class ModelWithPreprocess, where the preprocessing is performed when calling fit and when calling predict. They then log the fitted model of the ModelWithPreprocess class as a pyfunc model.
Which of the following is a benefit of this approach when loading the logged pyfunc model for downstream deployment?

Answer: E


NEW QUESTION # 185
A data scientist has created a Python function compute_features that returns a Spark DataFrame with the following schema:

The resulting DataFrame is assigned to the features_df variable. The data scientist wants to create a Feature Store table using features_df.
Which of the following code blocks can they use to create and populate the Feature Store table using the Feature Store Client fs?

Answer: B


NEW QUESTION # 186
A data scientist wants to track the runs of their random forest model. The data scientist is changing the number of trees and the maximum depth of the trees in the forest across each run.
They write the following code block:

Which Python object type does params need to be an instance of?

Answer: B

Explanation:
The params variable must be a dictionary (dict) because mlflow.log_params() expects a dictionary where each key-value pair represents a parameter name and its corresponding value.
Additionally, the model instantiation RandomForestRegressor(**params) also requires params to be a dictionary to unpack the parameters correctly.


NEW QUESTION # 187
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