- A
Increase the number of features
Why wrong: Adding more features increases model complexity and can worsen overfitting.
- B
Increase the regularization strength
Stronger regularization (e.g., higher L2 penalty) shrinks coefficients and reduces overfitting.
- C
Use a more complex model like XGBoost
Why wrong: More complex models are more prone to overfitting, not less.
- D
Use stratified cross-validation
Why wrong: Stratified CV helps evaluate generalization but does not directly reduce overfitting.
Question 595 of 1,000
hardMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data scientist trained a logistic regression…
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 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 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
✓
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.
- ✓
Increase the regularization strength
Why this is correct
Stronger regularization (e.g., higher L2 penalty) shrinks coefficients and reduces overfitting.
Clue confirmation
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.
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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: 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.
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Last reviewed: Jun 30, 2026
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