AIF-C01 Fundamentals of AI and ML Practice Question
An organization wants to detect anomalies in real-time streaming data from IoT devices. The data includes sensor readings, and the team plans to use a machine learning model. Which AWS service should be used to build and deploy the model with minimal operational overhead?
⚠ Common exam trap
AWS often tests the misconception that Amazon Kinesis Data Analytics can build and deploy custom ML models, when in fact it only supports built-in ML functions for simple anomaly detection and cannot train or host custom models.
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
✓
Amazon SageMaker
Amazon SageMaker is the correct choice because it provides a fully managed environment for building, training, and deploying machine learning models at scale. For real-time anomaly detection on streaming IoT data, SageMaker can host a trained model as a real-time endpoint that processes incoming sensor readings via Amazon Kinesis Data Streams or AWS Lambda, minimizing operational overhead by handling infrastructure, scaling, and monitoring automatically.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon SageMaker
Why this is correct
Amazon SageMaker provides fully managed infrastructure for building, training and deploying models, with built-in algorithms suited to streaming anomaly detection. It satisfies the stem's minimal operational overhead constraint by handling provisioning, scaling and endpoint hosting, letting the team focus on the model rather than servers.
- ✗
AWS Glue
Why it's wrong here
AWS Glue is a serverless ETL and data catalogue service for batch transformation and crawlers, not model training or real-time inference. It would be correct for preparing and cataloguing training data at rest; here the requirement is streaming anomaly detection with minimal operational overhead, which Amazon SageMaker or Lookout for Equipment addresses.
- ✗
Amazon QuickSight
Why it's wrong here
Amazon QuickSight is a business intelligence service for dashboards and visual analysis of stored data; it neither trains models nor scores streaming sensor readings. It would be correct for visualising anomaly results after the fact, but the requirement is real-time detection, which needs a streaming ML service.
- ✗
Amazon Kinesis Data Analytics
Why it's wrong here
Amazon Kinesis Data Analytics processes and analyses streaming data with SQL or Apache Flink, but does not itself build, train, or deploy a machine learning model with minimal operational overhead. It would be correct for continuous SQL aggregation over streams; the stem requires model training and deployment.
Quick reference
Cloud Service Model Comparison
| Model | You Manage | Provider Manages | Examples |
|---|---|---|---|
| IaaS | OS, runtime, apps, data | Hardware, hypervisor, networking | EC2, Azure VMs, GCP Compute Engine |
| PaaS | Apps and data | OS, runtime, middleware, hardware | Elastic Beanstalk, Azure App Service |
| SaaS | Data and settings only | Everything else | Microsoft 365, Salesforce, Workday |
| FaaS / Serverless | Function code only | Infra, scaling, runtime | Lambda, Azure Functions, Cloud Run |
| CaaS | Containers and apps | Kubernetes, OS, hardware | EKS, AKS, GKE |
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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.