The answer is data augmentation and transfer learning from a pre-trained model on ImageNet. These two techniques directly address the challenge of improving model generalization without collecting more data by artificially expanding the training distribution and leveraging learned feature representations. Data augmentation applies random transformations like crops, flips, and rotations to create diverse variations of existing images, effectively increasing dataset diversity without new samples. Transfer learning uses a model pre-trained on a large dataset like ImageNet, then fine-tunes it on the target task, which provides strong feature extractors that generalize well even with limited data. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of practical generalization strategies versus common pitfalls—traps include increasing batch size or learning rate, which can harm convergence or overfit, and simply adding more epochs, which risks overfitting without new data. A helpful memory tip: “Augment and transfer—no new data, better generalizer.”
MLS-C01 Modeling Practice Question
This MLS-C01 practice question tests your understanding of modeling. 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 developing a deep learning model for object detection using Amazon SageMaker. The training dataset has 50,000 labeled images. The data scientist wants to improve model generalization without collecting more data. Which TWO techniques can be applied? (Choose two.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Apply data augmentation techniques such as random cropping and horizontal flipping.
Options A and D are correct. Option A: Data augmentation (e.g., random crops, flips) effectively increases dataset diversity. Option D: Using a pre-trained model (transfer learning) improves generalization. Option B (increasing batch size) may hurt generalization. Option C (increasing learning rate) can cause divergence. Option E (increasing epochs) may lead to overfitting.
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 learning rate to speed up convergence.
Why it's wrong here
Higher learning rate can cause instability and not improve generalization.
✗
Increase the number of training epochs to ensure convergence.
Why it's wrong here
More epochs can lead to overfitting, not generalization.
✓
Apply data augmentation techniques such as random cropping and horizontal flipping.
Why this is correct
Data augmentation increases data diversity without new data.
Related concept
Read the scenario before looking for a memorised answer.
✓
Use transfer learning from a pre-trained model on ImageNet.
Why this is correct
Transfer learning leverages learned features, improving generalization.
Related concept
Read the scenario before looking for a memorised answer.
✗
Increase the batch size to reduce variance.
Why it's wrong here
Larger batch sizes often lead to poorer generalization.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Apply data augmentation techniques such as random cropping and horizontal flipping. — Options A and D are correct. Option A: Data augmentation (e.g., random crops, flips) effectively increases dataset diversity. Option D: Using a pre-trained model (transfer learning) improves generalization. Option B (increasing batch size) may hurt generalization. Option C (increasing learning rate) can cause divergence. Option E (increasing epochs) may lead to overfitting.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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.