Question 1easymultiple choice
Full question →easymultiple choiceObjective-mapped
A retail company wants to automatically group its customers into distinct segments based on their purchasing patterns, without having pre-defined categories. The goal is to discover natural groupings in the customer data to tailor marketing campaigns. Which type of machine learning task should the company use?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
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Supervised learning - Classification
Classification is a supervised learning task that assigns data points to predefined categories using labeled training data. Since the company has no predefined categories, this is not applicable.
B
Best answer
Unsupervised learning - Clustering
Clustering is an unsupervised learning technique that groups similar data points together based on features, without needing labels. This fits the scenario of discovering natural customer segments from purchasing patterns.
C
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Reinforcement learning
Reinforcement learning trains models to make sequences of decisions by rewarding desired behaviors. It is not used for grouping static data but for interactive environments like games or robotics.
D
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Supervised learning - Regression
Regression predicts a continuous numeric value, such as price or sales amount. It requires labeled data and does not produce discrete groups of customers.
Common exam trap
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.
Technical deep dive
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.
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
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Question 5
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Question 6
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FAQ
Questions learners often ask
What does this AI-900 question test?
Static NAT maps one inside address to one outside address.
What is the correct answer to this question?
The correct answer is: Unsupervised learning - Clustering — Unsupervised learning is used when the data has no labels and the model must find patterns or groupings on its own. Clustering is a common unsupervised technique to segment customers. Supervised learning requires labeled data, which is not available here. Reinforcement learning is for decision-making in environments with rewards. Regression predicts a numeric value, not groups.
What should I do if I get this AI-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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