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

SectionWeightObjectives
Topic 1: Model Lifecycle Management (MLOps)43%- Automated Workflows
  • 1. Automated retraining workflows
  • 2. CI/CD pipelines for ML
- Testing Strategies
  • 1. Data quality testing
  • 2. Model testing and validation
- Monitoring
  • 1. Model performance monitoring
  • 2. Lakehouse Monitoring for drift detection
- Environment Management
  • 1. Environment configuration and reproducibility
  • 2. Databricks Asset Bundles (DABs)
Topic 2: Model Development47%- Feature Store
  • 1. Feature Store concepts and usage
  • 2. Automated feature pipelines
- MLflow Advanced Features
  • 1. Experiment tracking and model management
  • 2. Model registry and versioning
- SparkML and Scaling
  • 1. Distributed training and hyperparameter tuning
  • 2. Building scalable ML pipelines with SparkML
  • 3. Ray/Optuna distributed tuning
Topic 3: Model Deployment10%- Deployment Strategies
  • 1. Streaming Deployment
  • 2. Batch Deployment
  • 3. Real-time Deployment
- Model Serving
  • 1. Custom serving solutions
  • 2. Databricks Model Serving

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Databricks Certified Machine Learning Professional Sample Questions (Q191-Q196):

NEW QUESTION # 191
Which of the following can be used to compare the relative prevalence of specific values in a single categorical variable between two datasets or time periods?

Answer: A

Explanation:
Contingency tables (also known as cross-tabulations) display the frequency distribution of categorical variables, allowing comparison of how specific category values occur across two datasets or time periods. This makes them effective for identifying categorical feature drift or shifts in value prevalence.


NEW QUESTION # 192
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?

Answer: B


NEW QUESTION # 193
A machine learning engineer has created a webhook with the following code block:

Which of the following code blocks will trigger this webhook to run the associate job?

Answer: E


NEW QUESTION # 194
A machine learning engineer wants to delete an active MLflow Model Registry Webhook with webhook ID webhook_id for a specific model.
They are using the following code block:

Which change does the machine learning engineer need to make to this code block so it will successfully accomplish the task?

Answer: D

Explanation:
To delete a webhook using the MLflow REST API, the correct endpoint is:
/api/2.0/mlflow/registry-webhooks/delete
In the provided code, the endpoint incorrectly uses "list", which retrieves webhooks rather than deleting them. The HTTP method "DELETE" is correct, so the only necessary change is to replace list with delete in the endpoint URL.


NEW QUESTION # 195
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?

Answer: C


NEW QUESTION # 196
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