- A
Use F1 score instead of accuracy
Why wrong: F1 is a metric, not an action to change recall.
- B
Decrease the threshold to 0.3
Lower threshold increases recall (more positives predicted) but may reduce precision.
- C
Apply oversampling to the minority class
Why wrong: Oversampling can improve model performance but does not directly adjust the threshold.
- D
Increase the threshold to 0.7
Why wrong: Increasing threshold reduces recall further.
Question 308 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. 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 binary classification model outputs probabilities. The default threshold of 0.5 results in high precision but low recall. Which action would likely increase recall while maintaining acceptable precision?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Decrease the threshold to 0.3
Decreasing the threshold to 0.3 makes the model classify more instances as positive, which increases recall (more true positives captured) but may also increase false positives. The goal is to shift the precision-recall trade-off toward higher recall while keeping precision at an acceptable level, which is directly achieved by lowering the decision threshold.
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 F1 score instead of accuracy
Why it's wrong here
F1 is a metric, not an action to change recall.
- ✓
Decrease the threshold to 0.3
Why this is correct
Lower threshold increases recall (more positives predicted) but may reduce precision.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Apply oversampling to the minority class
Why it's wrong here
Oversampling can improve model performance but does not directly adjust the threshold.
- ✗
Increase the threshold to 0.7
Why it's wrong here
Increasing threshold reduces recall further.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that changing the evaluation metric (like F1 score) or resampling the data (like oversampling) directly adjusts the model's output threshold, when in fact only threshold tuning changes the classification boundary after training.
Detailed technical explanation
How to think about this question
The decision threshold is a hyperparameter applied to the model's raw probability output; lowering it from 0.5 to 0.3 means the model will predict the positive class for any instance with probability ≥ 0.3. This directly increases the true positive rate (recall) at the cost of potentially more false positives, which reduces precision. In practice, the optimal threshold is often chosen by analyzing the precision-recall curve or ROC curve to balance the business cost of false negatives versus false positives.
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
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
AI and ML Fundamentals — This question tests AI and ML Fundamentals — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Decrease the threshold to 0.3 — Decreasing the threshold to 0.3 makes the model classify more instances as positive, which increases recall (more true positives captured) but may also increase false positives. The goal is to shift the precision-recall trade-off toward higher recall while keeping precision at an acceptable level, which is directly achieved by lowering the decision threshold.
What should I do if I get this AIF-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.
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Last reviewed: Jul 4, 2026
This AIF-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 AIF-C01 exam.
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