- A
Increase the number of hidden layers to five to capture more complex patterns
Why wrong: More layers increase complexity but may not improve recall; could cause overfitting.
- B
Use synthetic minority oversampling (SMOTE) to rebalance the training set
Why wrong: The model was trained on a balanced set; resampling may not help and threshold adjustment is simpler.
- C
Change the threshold for classifying a transaction as fraud from the default 0.5 to a lower value
Lowering threshold increases TPR; the optimal threshold can be chosen based on the precision-recall curve.
- D
Add L2 regularization to reduce overfitting
Why wrong: Regularization increases bias, likely decreasing TPR further.
Question 836 of 1,000
AI0-001 Machine Learning and Deep Learning Practice Question
This AI0-001 practice question tests your understanding of machine learning and deep learning. 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 financial institution uses a deep learning model for fraud detection. The model is a feedforward neural network with three hidden layers. It was trained on a balanced dataset of 100,000 transactions. During deployment, the model achieves high accuracy on the test set but the fraud detection rate (true positive rate) is only 40% while the false positive rate is 0.1%. The business requires a true positive rate of at least 80%. Which of the following actions is most likely to achieve the required true positive rate while minimizing the increase in false positives?
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:
"least"Why it matters: You want the option with minimum overhead, fewest steps, or lowest impact — not the most feature-rich or comprehensive answer.
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
✓
Change the threshold for classifying a transaction as fraud from the default 0.5 to a lower value
Option A (increase hidden layers) may capture more complexity but does not directly increase TPR and could overfit. Option B (SMOTE) rebalances the training set, but the dataset is already balanced, so this is unlikely to improve TPR. Option D (L2 regularization) reduces overfitting but increases bias, which could lower TPR. Option C (change threshold) is the most direct approach: lowering the classification threshold increases the true positive rate, and by tuning, it can achieve 80% TPR with a minimal increase in false positives.
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 number of hidden layers to five to capture more complex patterns
Why it's wrong here
More layers increase complexity but may not improve recall; could cause overfitting.
- ✗
Use synthetic minority oversampling (SMOTE) to rebalance the training set
Why it's wrong here
The model was trained on a balanced set; resampling may not help and threshold adjustment is simpler.
- ✓
Change the threshold for classifying a transaction as fraud from the default 0.5 to a lower value
Why this is correct
Lowering threshold increases TPR; the optimal threshold can be chosen based on the precision-recall curve.
Clue confirmation
The clue words "most likely", "least" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Add L2 regularization to reduce overfitting
Why it's wrong here
Regularization increases bias, likely decreasing TPR further.
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 practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
Quick reference
OSI Model Reference
| Layer | Name | PDU | Key Protocols / Devices |
|---|---|---|---|
| 7 | Application | Data | HTTP, HTTPS, DNS, SMTP, FTP, SSH |
| 6 | Presentation | Data | TLS / SSL, JPEG, ASCII encoding |
| 5 | Session | Data | NetBIOS, RPC, SIP |
| 4 | Transport | Segment / Datagram | TCP, UDP |
| 3 | Network | Packet | IP, ICMP, OSPF — Routers |
| 2 | Data Link | Frame | Ethernet, Wi-Fi, PPP — Switches, Bridges |
| 1 | Physical | Bits | Cables, NICs, Hubs, Repeaters |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this AI0-001 question test?
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Change the threshold for classifying a transaction as fraud from the default 0.5 to a lower value — Option A (increase hidden layers) may capture more complexity but does not directly increase TPR and could overfit. Option B (SMOTE) rebalances the training set, but the dataset is already balanced, so this is unlikely to improve TPR. Option D (L2 regularization) reduces overfitting but increases bias, which could lower TPR. Option C (change threshold) is the most direct approach: lowering the classification threshold increases the true positive rate, and by tuning, it can achieve 80% TPR with a minimal increase in false positives.
What should I do if I get this AI0-001 question wrong?
Identify which AI0-001 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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely", "least". 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.
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Last reviewed: Jun 23, 2026
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