Quick Answer
The answer is to use a pre-trained model as the backbone, such as ResNet-50 pre-trained on ImageNet. This is the most effective way to improve mAP for object detection without increasing training time because transfer learning leverages features already learned from a large, diverse dataset, allowing the model to converge to a higher accuracy plateau from the start. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how to optimize deep learning training within SageMaker’s built-in algorithms, where the common trap is to assume that tuning hyperparameters like batch size or learning rate will fix a stalled mAP, when the real bottleneck is insufficient feature extraction from scratch. A key memory tip: when loss plateaus but mAP is low, think “backbone boost”—a pre-trained backbone gives your model a head start without adding training time.
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 research team is training a deep learning model for object detection using SageMaker's built-in SSD algorithm. The dataset contains 50,000 images with bounding box annotations. The team uses a single ml.p3.2xlarge instance. After 24 hours of training, the model's loss has plateaued, but the mean average precision (mAP) on validation is only 0.45. The team wants to improve mAP without increasing training time. Which action should they take?
Increase the learning rate by a factor of 2
Why wrong: Higher learning rate may cause instability.
Use a pre-trained model as the backbone (e.g., ResNet-50 pre-trained on ImageNet)
Transfer learning boosts accuracy with no additional training time.
Increase the batch size to 64
Why wrong: Larger batch may not improve mAP and could slow convergence.
Add more convolutional layers to the backbone
Why wrong: More layers increase training time.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
Use a pre-trained model as the backbone (e.g., ResNet-50 pre-trained on ImageNet)
Option B (use a pre-trained backbone) transfers learned features, often improving accuracy. Option A (increase batch size) may not improve mAP and could slow convergence. Option C (add more layers) increases training time. Option D (increase learning rate) may destabilize training.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Increase the learning rate by a factor of 2
Why it's wrong here
Higher learning rate may cause instability.
Use a pre-trained model as the backbone (e.g., ResNet-50 pre-trained on ImageNet)
Why this is correct
Transfer learning boosts accuracy with no additional training time.
Related concept
Read the scenario before looking for a memorised answer.
Increase the batch size to 64
Why it's wrong here
Larger batch may not improve mAP and could slow convergence.
Add more convolutional layers to the backbone
Why it's wrong here
More layers increase training time.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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.
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FAQ
Modeling — This question tests Modeling — Read the scenario before looking for a memorised answer..
The correct answer is: Use a pre-trained model as the backbone (e.g., ResNet-50 pre-trained on ImageNet) — Option B (use a pre-trained backbone) transfers learned features, often improving accuracy. Option A (increase batch size) may not improve mAP and could slow convergence. Option C (add more layers) increases training time. Option D (increase learning rate) may destabilize training.
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.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
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.
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