- A
Use Amazon Rekognition Custom Labels to detect anomalies in sensor images
Why wrong: Amazon Rekognition is for image and video analysis, not IoT sensor time-series data.
- B
Train a supervised classifier using Amazon SageMaker; use Amazon Kinesis Data Analytics for real-time inference
Why wrong: Supervised learning requires labeled data, which is not available. Also, Kinesis Data Analytics is for SQL-based analytics, not ML inference.
- C
Apply k-means clustering with Amazon SageMaker; deploy the model as a real-time endpoint
Why wrong: K-means is an unsupervised method that does not explicitly model rare anomalies; it groups data into clusters and may not effectively detect rare events.
- D
Use a semi-supervised one-class classifier with Amazon Lookout for Equipment
Amazon Lookout for Equipment is designed for sensor data and can train a model on normal operating data (semi-supervised) to detect anomalies in real time.
AIF-C01 Practice Question: A team is building a real-time anomaly detection…
This AIF-C01 practice question tests your understanding of aif-c01 exam topics. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 team is building a real-time anomaly detection system for IoT sensor data. The data is unlabeled, and the team expects the anomalies to be rare but of high importance. Which combination of approach and AWS service should the team use?
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
Use a semi-supervised one-class classifier with Amazon Lookout for Equipment
For unlabeled data with rare anomalies, a semi-supervised approach using a one-class classifier is appropriate because it learns the 'normal' pattern. Amazon Lookout for Equipment is designed for sensor data anomaly detection and can be used in a semi-supervised manner. The other options require labels or are not suited for real-time IoT data.
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.
- ✗
Use Amazon Rekognition Custom Labels to detect anomalies in sensor images
Why it's wrong here
Amazon Rekognition is for image and video analysis, not IoT sensor time-series data.
- ✗
Train a supervised classifier using Amazon SageMaker; use Amazon Kinesis Data Analytics for real-time inference
Why it's wrong here
Supervised learning requires labeled data, which is not available. Also, Kinesis Data Analytics is for SQL-based analytics, not ML inference.
- ✗
Apply k-means clustering with Amazon SageMaker; deploy the model as a real-time endpoint
Why it's wrong here
K-means is an unsupervised method that does not explicitly model rare anomalies; it groups data into clusters and may not effectively detect rare events.
- ✓
Use a semi-supervised one-class classifier with Amazon Lookout for Equipment
Why this is correct
Amazon Lookout for Equipment is designed for sensor data and can train a model on normal operating data (semi-supervised) to detect anomalies in real time.
Related concept
Static NAT maps one inside address to one outside address.
Common exam traps
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 AIF-C01 NAT questions on configuration and troubleshooting.
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FAQ
Questions learners often ask
What does this AIF-C01 question test?
Static NAT maps one inside address to one outside address.
What is the correct answer to this question?
The correct answer is: Use a semi-supervised one-class classifier with Amazon Lookout for Equipment — For unlabeled data with rare anomalies, a semi-supervised approach using a one-class classifier is appropriate because it learns the 'normal' pattern. Amazon Lookout for Equipment is designed for sensor data anomaly detection and can be used in a semi-supervised manner. The other options require labels or are not suited for real-time IoT data.
What should I do if I get this AIF-C01 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 AIF-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Last reviewed: Jul 4, 2026
This AIF-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AIF-C01 exam.
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