AI0-001 Implementing AI Solutions Practice Question
A machine learning engineer is deploying a real-time anomaly detection system for manufacturing sensor data. The system must process thousands of readings per second with minimal latency. Which deployment architecture is BEST suited?
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
✓
AI microservices with an async processing queue and streaming responses
AI microservices with async processing queues and streaming responses can handle high throughput and low latency for real-time data.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Batch processing using Apache Spark jobs triggered hourly
Why it's wrong here
Batch processing is not suitable for real-time anomaly detection requiring immediate response.
- ✗
Serverless functions deployed on a CDN
Why it's wrong here
Serverless functions may have cold start latency and are not optimized for streaming sensor data.
- ✗
A monolithic web application with a relational database
Why it's wrong here
Monolithic architecture cannot scale to handle high-frequency sensor data with low latency.
- ✓
AI microservices with an async processing queue and streaming responses
Why this is correct
Microservices with async queues and streaming allow scalable, low-latency processing of high-throughput data.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA 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 AI0-001 exam.