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
Option E, a convolutional neural network (CNN), is correct because CNNs apply learned spatial filters over pixel grids and are the standard architecture for per-frame object detection and feature extraction in video surveillance, efficiently capturing spatial hierarchies in each image. Option D, an RNN or LSTM, is correct because recurrent architectures model temporal dependencies across sequential frames, allowing the system to learn movement patterns and object trajectories over time; LSTMs in particular mitigate vanishing gradients for longer sequences. Together, a CNN front end for spatial detection plus an RNN/LSTM back end for temporal tracking forms a classic video-analysis pipeline. Option A, a Transformer encoder only, is not the intended pairing here since it lacks the convolutional spatial inductive bias for frame-level detection and, used alone, does not provide the recurrent temporal modeling this scenario calls for. Option B, a GAN, is for generative adversarial training to synthesize or enhance data, not for detection and tracking. Option C, a VAE, is a generative model for learning latent representations and reconstruction, not a supervised detector-tracker component.
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
A GAN generates realistic synthetic images through adversarial training; it performs no object detection or cross-frame tracking, so it cannot satisfy either requirement. It tempts because GANs are used to augment scarce surveillance training data, but the task needs a detector plus a temporal tracking model.
- ✗
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 and LSTMs maintain hidden state across timesteps, so they model temporal dependencies between frames — exactly the movement-tracking requirement. Combined with a CNN for per-frame object detection, they satisfy the stem's dual constraint of detecting objects and tracking motion patterns over time.
- ✓
Convolutional neural network (CNN)
Why this is correct
CNNs apply convolutional filters that extract spatial features such as edges and shapes from each frame, satisfying the per-frame object-detection requirement. Paired with an RNN or LSTM to model temporal dependencies, they cover the stem's need to track movement patterns across successive frames.
Visual reference
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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.