- A
Train a supervised machine learning model on historical decisions
A supervised model can learn approval patterns and adapt to new data without manual rule updates.
- B
Continue refining the rules-based system manually
Why wrong: Refining manually is not scalable with frequent regulation changes.
- C
Implement a deep neural network without feature engineering
Why wrong: Deep learning may be overkill and requires careful tuning; supervised ML is simpler.
- D
Use unsupervised learning to cluster applications
Why wrong: Unsupervised learning does not predict approval decisions.
Question 38 of 1,000
AIF-C01 AI and ML Fundamentals Practice Question
This AIF-C01 practice question tests your understanding of ai and ml fundamentals. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 wants to use a rules-based approach to approve loan applications but finds that it cannot keep up with changing regulations. They have historical data with decisions. Which approach should they adopt?
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
✓
Train a supervised machine learning model on historical decisions
Option A is correct because supervised machine learning can automatically learn patterns from historical loan decision data, adapting to changing regulations without manual rule updates. By training a model on labeled examples of approved and rejected applications, the system can generalize to new cases and adjust as the underlying regulatory logic shifts over time, which is precisely the limitation of a static rules-based approach.
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.
- ✓
Train a supervised machine learning model on historical decisions
Why this is correct
A supervised model can learn approval patterns and adapt to new data without manual rule updates.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Continue refining the rules-based system manually
Why it's wrong here
Refining manually is not scalable with frequent regulation changes.
- ✗
Implement a deep neural network without feature engineering
Why it's wrong here
Deep learning may be overkill and requires careful tuning; supervised ML is simpler.
- ✗
Use unsupervised learning to cluster applications
Why it's wrong here
Unsupervised learning does not predict approval decisions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between supervised and unsupervised learning by presenting a scenario where historical labels exist, tempting candidates to choose unsupervised clustering (Option D) because they confuse 'grouping similar applications' with 'predicting approval decisions.'
Detailed technical explanation
How to think about this question
Supervised learning for loan approval typically uses algorithms like logistic regression, gradient-boosted trees (e.g., XGBoost), or random forests, which can handle mixed data types and provide interpretable feature importance. The model learns a decision boundary from historical data, and retraining on new data with updated labels allows it to implicitly capture regulatory changes without explicit rule rewriting—a key advantage in dynamic compliance environments.
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 healthcare organisation deploys an application with a public-facing web tier and a private database tier. The database subnet has no public IP and only accepts connections from the web tier's security group. Questions like this test whether you can design cloud network isolation using VNets/VPCs, subnets, and security group rules.
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: Train a supervised machine learning model on historical decisions — Option A is correct because supervised machine learning can automatically learn patterns from historical loan decision data, adapting to changing regulations without manual rule updates. By training a model on labeled examples of approved and rejected applications, the system can generalize to new cases and adjust as the underlying regulatory logic shifts over time, which is precisely the limitation of a static rules-based approach.
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
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