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

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

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Databricks Certified Machine Learning Professional Sample Questions (Q108-Q113):

NEW QUESTION # 108
A machine learning engineering team wants to build a continuous pipeline for data preparation of a machine learning application. The team would like the data to be fully processed and made ready for inference in a series of equal-sized batches. Which tool can be used to provide this type of continuous processing?

Answer: B


NEW QUESTION # 109
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: D


NEW QUESTION # 110
Which MLflow command logs a trained model?

Answer: D

Explanation:
mlflow.log_model() saves the model artifact within the run.


NEW QUESTION # 111
Which of the following is a reason for using Jensen-Shannon (JS) distance over a Kolmogorov-Smirnov (KS) test for numeric feature drift detection?

Answer: D


NEW QUESTION # 112
A data scientist is using MLflow to track their machine learning experiment. As a part of each MLflow run, they are performing hyperparameter tuning. The data scientist would like to have one parent run for the tuning process with a child run for each unique combination of hyperparameter values.
They are using the following code block:

The code block is not nesting the runs in MLflow as they expected.
Which of the following changes does the data scientist need to make to the above code block so that it successfully nests the child runs under the parent run in MLflow?

Answer: E


NEW QUESTION # 113
......

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