MLA-C01 Data Preparation for Machine Learning Practice Question
Which TWO actions are recommended best practices when preparing training data for a machine learning model in AWS? (Choose two.)
⚠ Common exam trap
Test-takers frequently assume all outliers must be removed (Option A) or that normalization is always required (Option E), but the exam tests nuanced understanding that these steps depend on the algorithm and data characteristics, not blanket rules.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Check for and handle missing values appropriately.
Missing values can introduce bias or cause algorithms to fail, so handling them (e.g., via imputation or removal) is a critical data preparation step in AWS SageMaker. Option D is correct because splitting data into training, validation, and test sets allows you to evaluate model performance on unseen data and prevent overfitting, which is a standard practice in SageMaker's built-in algorithms and training jobs.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Remove all outliers from the dataset.
Why it's wrong here
Outliers may contain valuable information; blind removal is not recommended.
- ✗
Train the model on the entire dataset to maximize data usage.
Why it's wrong here
No held-out data leads to overfitting and inability to evaluate.
- ✓
Check for and handle missing values appropriately.
Why this is correct
Missing values can cause errors or bias if not addressed.
- ✓
Split the data into training, validation, and test sets.
Why this is correct
Proper splits are crucial for unbiased performance estimation.
- ✗
Always normalize all features to a [0,1] range.
Why it's wrong here
Normalization is not needed for all algorithms (e.g., decision trees).
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.