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

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

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Databricks Certified Machine Learning Professional Sample Questions (Q116-Q121):

NEW QUESTION # 116
Which statement is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov- Smirnov (KS) test for numeric feature drift detection?

Answer: E


NEW QUESTION # 117
Which is a benefit of logging an input example with an MLflow model?

Answer: D

Explanation:
Logging an input example with an MLflow model provides a concrete sample of the data format expected by the model. This helps serving applications and users understand the model's input structure and verify that inference requests are properly formatted when deploying or testing the model in production.


NEW QUESTION # 118
A data scientist wants to remove the star_rating column from the Delta table at the location path.
To do this, they need to load in data and drop the star_rating column. Which of the following code blocks accomplishes this task?

Answer: B


NEW QUESTION # 119
A machine learning engineer has implemented a numeric drift monitoring solution by examining trends in the summary statistics of input variables. However, the engineer's stakeholders would like a more robust monitoring solution. Which of the following can provide a more robust drift monitoring solution for numeric feature variables?

Answer: B

Explanation:
Statistical tests (such as the Kolmogorov-Smirnov test or Wasserstein distance) provide a more robust and quantitative method for detecting numeric feature drift compared to simple summary statistics. These tests compare the full distributions of features between datasets, making them more sensitive to subtle changes in data behavior over time.


NEW QUESTION # 120
Which of the following deployment paradigms can centrally compute predictions for a single record with exceedingly fast results?

Answer: B


NEW QUESTION # 121
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