AI0-001 AI Concepts and Techniques Practice Question
An AI developer is selecting a model architecture for a real-time video surveillance system that must detect objects in each frame and also track movement patterns across frames. Which TWO architectures should the developer combine? (Choose 2)
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
✓
Recurrent neural network (RNN) or LSTM
CNNs are ideal for image feature extraction; RNNs/LSTMs are designed for sequence modelling to track temporal patterns.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Transformer encoder only
Why it's wrong here
A Transformer encoder alone lacks a temporal modelling mechanism, such as recurrent connections or attention over time, which is required to track movement patterns across frames in a video stream. It processes each frame independently, so it cannot encode the sequential dependencies needed for motion analysis. This option is tempting because Transformer encoders excel at spatial feature extraction in static images, making them a correct choice for single-frame object detection tasks.
- ✗
Generative adversarial network (GAN)
Why it's wrong here
GANs are for generation, not detection or tracking.
- ✗
Variational autoencoder (VAE)
Why it's wrong here
VAEs are also for generation, not detection.
- ✓
Recurrent neural network (RNN) or LSTM
Why this is correct
RNNs/LSTMs capture temporal dependencies across frames.
- ✓
Convolutional neural network (CNN)
Why this is correct
CNNs excel at spatial feature extraction from images.
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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.