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 is using Amazon SageMaker to train a linear regression model. After training, the scientist notices that the training and validation errors are both low, but the model performs poorly on new test data. What is the MOST likely cause?
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
✓
There is data leakage from the validation set into the training set
Option A is correct because data leakage from the validation set into the training set would allow the model to learn patterns that are not present in truly unseen data, leading to artificially low training and validation errors but poor generalization to new test data. In SageMaker, this can occur if the dataset is not properly split before feature engineering or if preprocessing (e.g., scaling or imputation) is applied to the entire dataset before splitting, causing the validation set to influence the training process.
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.
✓
There is data leakage from the validation set into the training set
Why this is correct
Data leakage artificially inflates performance on validation but fails on true unseen data.
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.
✗
The features are not scaled properly
Why it's wrong here
Improper scaling affects convergence speed but not generalization performance if model converged.
✗
The model is overfitting the training data
Why it's wrong here
Overfitting would cause low training error but high validation error, not low validation error.
✗
The model has high bias
Why it's wrong here
High bias would result in high training error as well.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse overfitting (low training error, high validation error) with data leakage (low training and validation errors, but poor test performance), so they incorrectly select Option C without recognizing that the validation error is also low.
Detailed technical explanation
How to think about this question
Data leakage often occurs when the same preprocessing pipeline (e.g., StandardScaler fit_transform) is applied to the entire dataset before splitting, allowing statistics like mean and variance from the validation set to leak into the training set. In SageMaker, using the built-in scikit-learn container or a custom script, this can happen if the train/validation split is performed after feature engineering or if cross-validation is not properly isolated. A real-world example is using target encoding on the full dataset before splitting, which introduces future information into the training set.
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
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 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: There is data leakage from the validation set into the training set — Option A is correct because data leakage from the validation set into the training set would allow the model to learn patterns that are not present in truly unseen data, leading to artificially low training and validation errors but poor generalization to new test data. In SageMaker, this can occur if the dataset is not properly split before feature engineering or if preprocessing (e.g., scaling or imputation) is applied to the entire dataset before splitting, causing the validation set to influence the training process.
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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Question Discussion
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