MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A data scientist is evaluating data quality for a machine learning project. The dataset has missing values, outliers, and inconsistent formatting. Which TWO steps should the data scientist perform during the data preparation phase? (Choose 2.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Standardize numeric features
Standardizing numeric features (Option C) is a critical data preparation step because it rescales features to have zero mean and unit variance, which prevents features with larger magnitudes from dominating distance-based algorithms like k-nearest neighbors or gradient descent optimization. This transformation is essential for many machine learning models to converge faster and perform correctly.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Normalize text data to lowercase
Why it's wrong here
Although sometimes useful, it is a specific step and not a general necessary part of data preparation for all datasets.
✗
Remove all outliers blindly
Why it's wrong here
Blind removal can discard valuable data and bias the model.
✓
Standardize numeric features
Why this is correct
Standardization (e.g., z-score) helps many algorithms converge faster.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a large neural network to handle all transformations
Why it's wrong here
Neural networks are models, not data preparation techniques.
✓
Impute missing values using mean or median
Why this is correct
Imputation preserves data size and is a common practice.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between data preparation steps that are universally applicable (like imputation and standardization) versus those that are task-specific or harmful (like blind outlier removal or using complex models for preprocessing), tempting candidates to choose options that seem plausible but are technically incorrect.
Detailed technical explanation
How to think about this question
Standardization (Z-score normalization) transforms each feature by subtracting the mean and dividing by the standard deviation, making the distribution have a mean of 0 and standard deviation of 1. This is particularly important for algorithms that assume normally distributed data, such as linear regression, logistic regression, and support vector machines, and it helps gradient descent converge faster by ensuring all features contribute equally to the loss function.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Standardize numeric features — Standardizing numeric features (Option C) is a critical data preparation step because it rescales features to have zero mean and unit variance, which prevents features with larger magnitudes from dominating distance-based algorithms like k-nearest neighbors or gradient descent optimization. This transformation is essential for many machine learning models to converge faster and perform correctly.
What should I do if I get this MLA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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