The answer is to increase the regularization strength. This is correct because the model’s training accuracy of 0.99 versus a validation accuracy of 0.75 is a textbook sign of overfitting, where the model has memorized noise in the training data rather than learning general patterns. Regularization, whether L1 (Lasso) or L2 (Ridge), adds a penalty to the loss function for large coefficients, directly constraining the model’s complexity and forcing it to generalize better to unseen data. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your ability to diagnose overfitting from accuracy gaps and select the most direct fix—a common trap is choosing to add more features or increase model complexity, which would worsen the problem. Remember the memory tip: “High train, low vali? Regularize your party.”
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 data scientist trained a logistic regression model on a dataset with 100 features. After training, the training accuracy is 0.99 but validation accuracy is 0.75. Which action is MOST likely to reduce overfitting?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Increase the regularization strength
The model shows high training accuracy (0.99) but significantly lower validation accuracy (0.75), which is a classic sign of overfitting. Increasing the regularization strength (e.g., L1 or L2 penalty) in logistic regression directly penalizes large coefficients, reducing the model's complexity and improving generalization. This is the most direct way to address overfitting in a logistic regression model.
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.
✗
Increase the number of features
Why it's wrong here
Adding more features increases model complexity and can worsen overfitting.
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use a more complex model like XGBoost
Why it's wrong here
More complex models are more prone to overfitting, not less.
✗
Use stratified cross-validation
Why it's wrong here
Stratified CV helps evaluate generalization but does not directly reduce overfitting.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that adding more data or using more complex models always improves performance, but here the correct answer is to increase regularization strength, which directly counters overfitting in a logistic regression model.
Detailed technical explanation
How to think about this question
In logistic regression, regularization adds a penalty term to the loss function (e.g., L2 regularization adds λ * Σ(w²)), which shrinks coefficient magnitudes toward zero. The regularization strength hyperparameter (often denoted as C in scikit-learn, where C = 1/λ) controls this trade-off: lower C values increase regularization, forcing the model to be simpler. In practice, tuning this hyperparameter via cross-validation is a standard step to find the optimal balance between bias and variance.
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.
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: Increase the regularization strength — The model shows high training accuracy (0.99) but significantly lower validation accuracy (0.75), which is a classic sign of overfitting. Increasing the regularization strength (e.g., L1 or L2 penalty) in logistic regression directly penalizes large coefficients, reducing the model's complexity and improving generalization. This is the most direct way to address overfitting in a logistic regression model.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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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 data scientist is training a linear regression model on a dataset with 10 features. After training, the model shows high training accuracy but poor test accuracy. Which of the following is the most likely cause?
easy
A.Data leakage
B.Feature scaling
✓ C.Overfitting
D.Underfitting
Why C: High training accuracy and poor test accuracy indicates overfitting. Underfitting would show poor training accuracy. Data leakage could cause high accuracy but not necessarily overfitting. Feature scaling is a preprocessing step and not directly a cause of this behavior.
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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.
Question Discussion
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