The answer is to adjust the decision threshold of the model to increase recall at the expense of precision. This works because the object detection algorithm outputs a probability score for each prediction, and the default threshold (typically 0.5) is optimized for balanced accuracy, not for minimizing false negatives. By lowering the threshold, the model classifies more items as defective, catching nearly all true defects—thus boosting recall—even though it may also flag some non-defective items (lowering precision). On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding that threshold tuning is a post-training hyperparameter, not a retraining or data augmentation fix; a common trap is to suggest collecting more defective images or changing the algorithm, when the simplest, most direct fix is threshold adjustment. Remember the memory tip: when recall is the goal, lower the bar—drop the threshold to catch more positives.
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 company is using SageMaker built-in object detection algorithm to detect defects in manufacturing images. The model is trained on 10,000 labeled images and achieves 95% accuracy. However, in production, the model misclassifies many defective items as non-defective (false negatives). The business requires recall > 90% for the defect class. Which action should they take?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Adjust the decision threshold of the model to increase recall at the expense of precision
Threshold tuning directly optimizes recall for a given class.
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.
✗
Use a different algorithm such as semantic segmentation
Why it's wrong here
Changing algorithm may not directly address recall.
✓
Adjust the decision threshold of the model to increase recall at the expense of precision
Why this is correct
Lowering the threshold increases recall for the positive class.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use SageMaker's Automatic Model Tuning to find better hyperparameters
Why it's wrong here
Tuning may help but threshold adjustment is the most direct way.
✗
Retrain the model with more images of non-defective items
Why it's wrong here
This may worsen the imbalance and reduce recall.
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: Adjust the decision threshold of the model to increase recall at the expense of precision — Threshold tuning directly optimizes recall for a given class.
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.
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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
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