- A
Increase the model's precision by raising the classification threshold
Why wrong: Raising the threshold would likely decrease recall, increasing false negatives and cost.
- B
Use a different algorithm that trades off recall for precision
Why wrong: Trading recall for precision would hurt the goal of reducing false negatives.
- C
Add more features to the model without changing the threshold
Why wrong: Adding features may or may not improve recall; it is not a direct lever and might not reduce false negatives efficiently.
- D
Increase the model's recall by lowering the classification threshold
Lowering the threshold captures more positives, improving recall and reducing the most costly errors (false negatives).
Question 19 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 company is building a model to predict loan default. They have historical data with 5% default rate. The model must minimize false negatives (missed defaults) because each default costs $50,000. False positives (incorrectly flagged defaults) cost $500 in customer service time. The model currently has a recall of 0.70 and precision of 0.80. Which of the following actions would MOST likely reduce the total cost?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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
✓
Increase the model's recall by lowering the classification threshold
The cost of a false negative ($50,000) is 100 times greater than a false positive ($500). Lowering the classification threshold increases recall (reduces false negatives) at the expense of precision (increases false positives). Given the extreme cost asymmetry, the net expected cost will decrease even if many more false positives occur, because each additional true positive saves $50,000 while each extra false positive costs only $500. Option D directly increases recall, which is the correct lever for this cost structure.
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 model's precision by raising the classification threshold
Why it's wrong here
Raising the threshold would likely decrease recall, increasing false negatives and cost.
- ✗
Use a different algorithm that trades off recall for precision
Why it's wrong here
Trading recall for precision would hurt the goal of reducing false negatives.
- ✗
Add more features to the model without changing the threshold
Why it's wrong here
Adding features may or may not improve recall; it is not a direct lever and might not reduce false negatives efficiently.
- ✓
Increase the model's recall by lowering the classification threshold
Why this is correct
Lowering the threshold captures more positives, improving recall and reducing the most costly errors (false negatives).
Clue confirmation
The clue words "most likely", "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the misconception that higher precision is always better, but in cost-sensitive scenarios with asymmetric costs, maximizing recall (even at the cost of precision) is the correct strategy to minimize total financial loss.
Detailed technical explanation
How to think about this question
The classification threshold determines the decision boundary for probability scores. Lowering the threshold makes the model more sensitive, capturing more positive cases (higher recall) but also increasing false positives (lower precision). In cost-sensitive learning, the optimal threshold is found by minimizing the expected cost: threshold = cost_FN / (cost_FN + cost_FP). Here, cost_FN = $50,000 and cost_FP = $500, giving an optimal threshold of 50,000 / 50,500 ≈ 0.99, meaning the model should classify a case as default only if the predicted probability exceeds 99%—but that would maximize recall at the expense of precision, consistent with lowering the threshold from a typical 0.5 to a much lower value to avoid missing defaults.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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.
- →
AI and ML Fundamentals — study guide chapter
Learn the concepts, then practise the questions
- →
AI and ML Fundamentals practice questions
Targeted practice on this topic area only
- →
All AIF-C01 questions
1,000 questions across all exam domains
- →
AWS Certified AI Practitioner AIF-C01 study guide
Full concept coverage aligned to exam objectives
- →
AIF-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AIF-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Applications of Foundation Models practice questions
Practise AIF-C01 questions linked to Applications of Foundation Models.
AI and ML Fundamentals practice questions
Practise AIF-C01 questions linked to AI and ML Fundamentals.
Security, Compliance, and Governance for AI Solutions practice questions
Practise AIF-C01 questions linked to Security, Compliance, and Governance for AI Solutions.
Fundamentals of AI and ML practice questions
Practise AIF-C01 questions linked to Fundamentals of AI and ML.
Fundamentals of Generative AI practice questions
Practise AIF-C01 questions linked to Fundamentals of Generative AI.
Generative AI and Foundation Models practice questions
Practise AIF-C01 questions linked to Generative AI and Foundation Models.
Guidelines for Responsible AI practice questions
Practise AIF-C01 questions linked to Guidelines for Responsible AI.
Security, Compliance and Governance for AI Solutions practice questions
Practise AIF-C01 questions linked to Security, Compliance and Governance for AI Solutions.
AIF-C01 fundamentals practice questions
Practise AIF-C01 questions linked to AIF-C01 fundamentals.
AIF-C01 scenario practice questions
Practise AIF-C01 questions linked to AIF-C01 scenario.
AIF-C01 troubleshooting practice questions
Practise AIF-C01 questions linked to AIF-C01 troubleshooting.
Practice this exam
Start a free AIF-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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: Increase the model's recall by lowering the classification threshold — The cost of a false negative ($50,000) is 100 times greater than a false positive ($500). Lowering the classification threshold increases recall (reduces false negatives) at the expense of precision (increases false positives). Given the extreme cost asymmetry, the net expected cost will decrease even if many more false positives occur, because each additional true positive saves $50,000 while each extra false positive costs only $500. Option D directly increases recall, which is the correct lever for this cost structure.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely", "minimum / minimize". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 →
Keep practising
More AIF-C01 practice questions
- A data scientist is fine-tuning a foundation model on Amazon Bedrock for a custom summarization task. Which THREE practi…
- A company uses a foundation model for real-time translation in a chat application. The latency is high. Which optimizati…
- A developer is using the Amazon Bedrock API to generate text. They notice that the model sometimes returns harmful conte…
- Refer to the exhibit. A user invokes Claude v2 using the AWS CLI. The response is truncated. What is the most likely cau…
- A company wants to automatically detect anomalies in server metrics. Which algorithm is most appropriate?
- A company uses Amazon SageMaker to train a model. The training job fails with 'InsufficientInstanceCapacity' error. What…
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.
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.