The answer is to standardize the features to have zero mean and unit variance. This is the most critical preprocessing step because logistic regression relies on gradient descent optimization, which is highly sensitive to feature scale; when features have different magnitudes, the cost function becomes elongated, leading to slow or unstable convergence. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this concept tests your understanding of how optimization algorithms interact with data preprocessing, often appearing in scenario-based questions where a dataset contains mixed units like age and income. A common trap is assuming logistic regression is scale-invariant like tree-based models, but its use of gradient-based solvers makes scaling essential. Memory tip: think of logistic regression as a hiker—it walks fastest on flat, even ground, so standardize to keep the cost surface round and smooth.
MLA-C01 ML Model Development Practice Question
This MLA-C01 practice question tests your understanding of ml model development. 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 team is developing a model to predict customer churn. The dataset has 10,000 samples with 20 features. The target variable is binary with 15% churn rate. The team wants to use logistic regression. Which data preprocessing step is MOST important to ensure proper convergence?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Standardize the features to have zero mean and unit variance
Logistic regression uses gradient descent or similar optimization algorithms that rely on the scale of the features. When features have different units or magnitudes, the cost function becomes elongated, causing slow or unstable convergence. Standardizing to zero mean and unit variance ensures that all features contribute equally to the gradient updates, leading to faster and more reliable convergence.
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.
✗
Remove correlated features to reduce multicollinearity
Why it's wrong here
Multicollinearity affects interpretability but not necessarily convergence of logistic regression.
✗
Impute missing values with the median
Why it's wrong here
Missing value imputation is important but not the most critical for convergence.
✗
Apply SMOTE to balance the classes
Why it's wrong here
SMOTE addresses class imbalance but does not affect convergence of logistic regression.
✓
Standardize the features to have zero mean and unit variance
Why this is correct
Standardization ensures gradient descent converges faster and avoids dominance by large-scale features.
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 misconception that class imbalance is the primary barrier to convergence, when in fact feature scaling is the fundamental requirement for optimization algorithms in logistic regression.
Detailed technical explanation
How to think about this question
Under the hood, logistic regression minimizes the log-loss using iterative methods like Newton-Raphson or stochastic gradient descent. Without standardization, features with larger scales dominate the gradient, causing the algorithm to take inefficient steps or oscillate. In practice, using a solver like 'lbfgs' or 'saga' in scikit-learn, standardization is especially important when regularization (e.g., L1 or L2) is applied, because the penalty term assumes all features are on a similar scale.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
ML Model Development — This question tests ML Model Development — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Standardize the features to have zero mean and unit variance — Logistic regression uses gradient descent or similar optimization algorithms that rely on the scale of the features. When features have different units or magnitudes, the cost function becomes elongated, causing slow or unstable convergence. Standardizing to zero mean and unit variance ensures that all features contribute equally to the gradient updates, leading to faster and more reliable convergence.
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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