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 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 (more hidden layers) may not improve recall and could overfit. Option C (L2 regularization) would increase bias, likely lowering TPR. Option D (SMOTE) rebalances training but the model already trained on balanced data; threshold adjustment is more direct. Option B (lower decision threshold) directly increases TPR at the cost of FPR; threshold can be tuned to achieve 80% TPR with minimal FPR increase.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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
Static NAT maps one inside address to one outside address.
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI0-001 NAT questions on configuration and troubleshooting.
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Static NAT maps one inside address to one outside address..
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 (more hidden layers) may not improve recall and could overfit. Option C (L2 regularization) would increase bias, likely lowering TPR. Option D (SMOTE) rebalances training but the model already trained on balanced data; threshold adjustment is more direct. Option B (lower decision threshold) directly increases TPR at the cost of FPR; threshold can be tuned to achieve 80% TPR with minimal FPR increase.
What should I do if I get this AI0-001 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI0-001 NAT questions on configuration and troubleshooting.
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?
Static NAT maps one inside address to one outside address.
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Question Discussion
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