Data Preparation for ML
Feature engineering, data preprocessing with SageMaker Processing, data labeling with Ground Truth, and handling imbalanced datasets.
Key Concepts
Feature engineering, data preprocessing with SageMaker Processing, data labeling with Ground Truth, and handling imbalanced datasets.
📝 Study Tips from Top Scorers
- ✓Know SageMaker Processing jobs for data preparation
- ✓Understand feature store and feature engineering patterns
- ✓Master data labeling workflows with Ground Truth
📊 Domain Weight: 28%
This domain accounts for 28% of all AWS MLA-C01 exam questions. This is one of the most important domains — invest extra study time here.
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❓ FAQ — Data Preparation for ML
How much of the AWS MLA-C01 exam is Data Preparation for ML?
Data Preparation for ML covers 28% of the AWS MLA-C01 exam, making it one of the most heavily weighted domains.
What topics are covered?
Feature engineering, data preprocessing with SageMaker Processing, data labeling with Ground Truth, and handling imbalanced datasets.
How should I study for this domain?
Focus on understanding core concepts like feature engineering, SageMaker, Ground Truth. Use ExamCert's practice questions filtered by domain, and review detailed explanations for each answer.