Question 1hardmultiple choice
Full question →hardmultiple choiceObjective-mapped
An online retailer wants to build a recommendation system that learns from user interactions. The system suggests a product, and if the user clicks it, it receives a positive reward; if ignored, a negative reward. Over time, the system learns to make better suggestions. Which type of machine learning best describes this approach?
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
Distractor review
Supervised learning
Supervised learning trains on labeled data where the correct output is known. In this scenario, there is no pre-labeled 'correct recommendation'; the system learns from delayed feedback, not from a training set of correct examples.
B
Distractor review
Unsupervised learning
Unsupervised learning finds hidden patterns or clusters in data without any labels. Here, the system receives explicit reward signals based on actions, which is not part of unsupervised learning.
C
Best answer
Reinforcement learning
Reinforcement learning is characterized by an agent that takes actions in an environment to maximize cumulative reward. The system learns from the rewards of user clicks, making this a classic RL use case.
D
Distractor review
Semi-supervised learning
Semi-supervised learning uses a small amount of labeled data with a larger amount of unlabeled data. This scenario does not involve any labeled dataset; learning is based solely on interaction rewards.
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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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: Reinforcement learning — Reinforcement learning (RL) involves an agent that learns by interacting with an environment and receiving rewards or penalties for its actions. In this scenario, the recommendation system (agent) learns from feedback (clicks/ignores) to optimize its policy. Supervised learning requires labeled input-output pairs, unsupervised learning finds patterns without labels, and semi-supervised uses a mix of labeled and unlabeled data, none of which fit the trial-and-error reward-based learning described.
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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