- A
MinMaxScaler to scale all features to [0,1]
Why wrong: MinMaxScaler is sensitive to outliers; the feature ranging 0-100,000 may have outliers that compress the scale of other features.
- B
RobustScaler to scale based on percentiles
Why wrong: RobustScaler is robust to outliers, but it does not guarantee zero mean and unit variance; StandardScaler is more standard for deep learning.
- C
No scaling is needed because gradient descent can handle different scales
Why wrong: Gradient descent converges slowly or may not converge if features have vastly different scales; scaling is essential.
- D
StandardScaler to standardize all features to zero mean and unit variance
StandardScaler handles varying scales well and is less affected by outliers than MinMaxScaler. It is a common choice for gradient-based optimization.
StandardScaler (Z-Score Normalization) for Gradient Descent
This MLA-C01 practice question tests your understanding of mla-c01 exam topics. 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 team is preparing a dataset for training a deep learning model. They notice that some features have very different scales: one feature ranges from 0 to 1, another from 0 to 100,000, and a third is a binary indicator (0/1). The model uses gradient descent. Which scaling method should be applied to ALL features to ensure stable and efficient training?
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
StandardScaler to standardize all features to zero mean and unit variance
StandardScaler (z-score standardization) centers features to mean 0 and unit variance. It is robust to outliers (compared to MinMaxScaler which is sensitive to extreme values) and works well with gradient descent when features have varying scales.
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.
- ✗
MinMaxScaler to scale all features to [0,1]
Why it's wrong here
MinMaxScaler is sensitive to outliers; the feature ranging 0-100,000 may have outliers that compress the scale of other features.
- ✗
RobustScaler to scale based on percentiles
Why it's wrong here
RobustScaler is robust to outliers, but it does not guarantee zero mean and unit variance; StandardScaler is more standard for deep learning.
- ✗
No scaling is needed because gradient descent can handle different scales
Why it's wrong here
Gradient descent converges slowly or may not converge if features have vastly different scales; scaling is essential.
- ✓
StandardScaler to standardize all features to zero mean and unit variance
Why this is correct
StandardScaler handles varying scales well and is less affected by outliers than MinMaxScaler. It is a common choice for gradient-based optimization.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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 MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: StandardScaler to standardize all features to zero mean and unit variance — StandardScaler (z-score standardization) centers features to mean 0 and unit variance. It is robust to outliers (compared to MinMaxScaler which is sensitive to extreme values) and works well with gradient descent when features have varying scales.
What should I do if I get this MLA-C01 question wrong?
Identify which MLA-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
1 more ways this is tested on MLA-C01
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A machine learning engineer needs to standardize features to have zero mean and unit variance before training a support vector machine. Which scaling method should they apply?
easy- ✓ A.StandardScaler
- B.Normalizer
- C.RobustScaler
- D.MinMaxScaler
Why A: StandardScaler transforms data to have zero mean and unit variance, which is required for SVM and many other algorithms.
Last reviewed: Jul 4, 2026
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.
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