The answer is to standardize both features to have zero mean and unit variance. This is the most critical preprocessing step because gradient descent is highly sensitive to feature scale; when features like square_footage (500–10,000) and num_bedrooms (1–10) have vastly different ranges, the loss function’s contours become elongated, causing the optimizer to oscillate or converge slowly. Feature scaling for gradient descent ensures each feature contributes proportionally to weight updates, leading to faster and more stable convergence. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this concept tests your understanding of optimization fundamentals—expect scenario-based questions where unscaled features lead to slow training or failure to converge. A common trap is assuming normalization (scaling to [0,1]) is always best, but standardization is preferred when features have different units or distributions. Memory tip: “Scale to zero mean, unit variance—gradient descent gains its assurance.”
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 machine learning engineer is building a regression model to predict house prices. The feature 'square_footage' has values ranging from 500 to 10,000, while 'num_bedrooms' ranges from 1 to 10. Which preprocessing step is most critical before training a model that uses gradient descent?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Standardize both features to have zero mean and unit variance.
Gradient descent is sensitive to the scale of features because it updates weights proportionally to the feature values. With 'square_footage' (500–10,000) and 'num_bedrooms' (1–10), the large range difference causes the loss function's contours to be elongated, leading to slow or unstable convergence. Standardizing both features to zero mean and unit variance ensures each feature contributes equally to the gradient updates, enabling faster and more reliable optimization.
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.
✓
Standardize both features to have zero mean and unit variance.
Why this is correct
Standardization brings features to a common scale, crucial for gradient descent.
Related concept
Read the scenario before looking for a memorised answer.
✗
Apply a logarithmic transformation to both features.
Why it's wrong here
Log transformation addresses skew, not scale differences across features.
✗
Encode the 'num_bedrooms' feature using one-hot encoding.
Why it's wrong here
One-hot encoding is for nominal categorical data, not for ordinal or continuous.
✗
Impute missing values using the mean of the feature.
Why it's wrong here
Missing values are not stated; the primary issue is scale.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between scaling for gradient-based optimizers versus other preprocessing steps like encoding or transformation, trapping candidates who confuse feature scaling with handling outliers or categorical data.
Detailed technical explanation
How to think about this question
Standardization (Z-score normalization) transforms each feature to have mean 0 and standard deviation 1, computed as (x - μ) / σ. This is critical for gradient descent because it makes the cost function's contour plots more spherical, allowing the optimizer to take direct steps toward the minimum rather than zigzagging. In practice, without scaling, learning rates must be very small to avoid divergence, significantly increasing training time, and models like linear regression with gradient descent may fail to converge entirely.
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 both features to have zero mean and unit variance. — Gradient descent is sensitive to the scale of features because it updates weights proportionally to the feature values. With 'square_footage' (500–10,000) and 'num_bedrooms' (1–10), the large range difference causes the loss function's contours to be elongated, leading to slow or unstable convergence. Standardizing both features to zero mean and unit variance ensures each feature contributes equally to the gradient updates, enabling faster and more reliable optimization.
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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