The correct answer is that feature scaling prevents features with larger magnitudes from dominating distance-based algorithms, and it also helps gradient descent converge faster. This is because algorithms like k-nearest neighbors and support vector machines rely on distance calculations, where unscaled features with larger numerical ranges would disproportionately influence the result, while scaling creates a more spherical contour of the loss function, allowing gradient descent to take direct steps toward the minimum rather than oscillating. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this concept often appears in questions about data preprocessing for models like linear regression or clustering, with a common trap being that tree-based algorithms like random forest do not require scaling since they split on thresholds, not distances. A useful memory tip is to think of scaling as leveling the playing field: if one feature is in dollars and another in years, scaling ensures neither dominates simply because of its unit.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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.
Which TWO of the following are benefits of feature scaling for machine learning algorithms?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Faster convergence of gradient descent
Option D is correct because feature scaling, typically via standardization (z-score) or min-max normalization, ensures that gradient descent converges faster. Without scaling, features with larger numerical ranges dominate the gradient updates, causing the algorithm to oscillate and require more iterations to reach the optimum. Scaling produces a more spherical contour of the loss function, allowing gradient descent to take more direct steps toward the minimum.
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.
✗
Eliminates the effect of outliers
Why it's wrong here
Scaling does not remove outliers; robust scaling may reduce but not eliminate.
✗
Reduces the need for feature selection
Why it's wrong here
Scaling does not eliminate irrelevant features.
✗
Improves performance of decision tree algorithms
Why it's wrong here
Decision trees are not affected by feature scaling.
✓
Faster convergence of gradient descent
Why this is correct
Scaling ensures all features contribute equally to the gradient.
Related concept
Read the scenario before looking for a memorised answer.
✓
Prevents features with larger magnitudes from dominating distance-based algorithms
Why this is correct
Algorithms like k-NN and SVM are sensitive to feature scales.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume feature scaling universally improves all algorithms, but Cisco specifically tests that tree-based models (like decision trees) are scale-invariant, making option C a common distractor.
Detailed technical explanation
How to think about this question
Under the hood, gradient descent updates weights using the learning rate times the partial derivative of the loss; unscaled features cause the gradient to be disproportionately large for features with larger magnitudes, leading to zigzagging updates. In practice, for algorithms like k-nearest neighbors (k-NN) or support vector machines (SVM) that rely on Euclidean distance, scaling prevents features with larger ranges (e.g., income in thousands vs. age in years) from dominating the distance calculation, ensuring each feature contributes proportionally. A subtle behavior is that scaling does not guarantee improved accuracy—it only stabilizes optimization and distance metrics; for tree-based models, scaling is unnecessary and can even be counterproductive if it alters interpretability of splits.
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
A cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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.
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Faster convergence of gradient descent — Option D is correct because feature scaling, typically via standardization (z-score) or min-max normalization, ensures that gradient descent converges faster. Without scaling, features with larger numerical ranges dominate the gradient updates, causing the algorithm to oscillate and require more iterations to reach the optimum. Scaling produces a more spherical contour of the loss function, allowing gradient descent to take more direct steps toward the minimum.
What should I do if I get this MLS-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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