Spotting Overfitting in SageMaker: High Training Accuracy, Low Test Accuracy
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 reviewing the training logs from a SageMaker training job. The model's loss decreases steadily and accuracy increases. However, when the model is evaluated on a holdout test set, the accuracy is only 0.65. Which issue does this behavior suggest?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The model is overfitting to the training data.
The model shows strong performance on the training set (loss decreases, accuracy increases) but poor generalization to the holdout test set (accuracy 0.65). This classic symptom indicates overfitting, where the model has memorized the training data rather than learning generalizable patterns. In SageMaker, this can occur when the model is too complex relative to the amount of training data, or when regularization techniques like dropout or L2 weight decay are insufficient.
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.
✓
The model is overfitting to the training data.
Why this is correct
High training accuracy but low test accuracy is classic overfitting.
Related concept
Read the scenario before looking for a memorised answer.
✗
The learning rate is too high.
Why it's wrong here
Loss decreasing suggests learning rate is appropriate.
✗
The model is underfitting the training data.
Why it's wrong here
Underfitting would show poor training performance.
✗
The training data is corrupted.
Why it's wrong here
No indication of corruption.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates see 'loss decreases and accuracy increases' and assume the model is training well, overlooking that the holdout test set reveals the true generalization failure—the AWS Machine Learning Specialty exam tests whether you can distinguish overfitting from underfitting or learning rate issues based on the divergence between training and test metrics.
Trap categories for this question
Command / output trap
Underfitting would show poor training performance.
Detailed technical explanation
How to think about this question
Overfitting is often exacerbated by models with high capacity (e.g., deep neural networks with many parameters) relative to dataset size. In SageMaker, built-in algorithms like XGBoost or TensorFlow offer hyperparameters such as `subsample`, `early_stopping_rounds`, or `weight_decay` to mitigate overfitting. A real-world scenario is training a large image classifier on a small dataset, where the model learns noise and artifacts instead of robust features, leading to a test accuracy plateau around 0.65 despite near-perfect training accuracy.
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.
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The model is overfitting to the training data. — The model shows strong performance on the training set (loss decreases, accuracy increases) but poor generalization to the holdout test set (accuracy 0.65). This classic symptom indicates overfitting, where the model has memorized the training data rather than learning generalizable patterns. In SageMaker, this can occur when the model is too complex relative to the amount of training data, or when regularization techniques like dropout or L2 weight decay are insufficient.
What should I do if I get this MLS-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.
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 →
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This MLS-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 MLS-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.