Ultimate Edge AI & IoT Interview Questionnaire (With Answers & Code Examples)

 

Introduction

Edge AI is revolutionizing the way devices process data in real-time, combining machine learning with IoT devices.

Unlike traditional cloud AI, edge AI performs computation on the device itself, reducing latency, improving privacy,

and enabling smarter autonomous systems. This questionnaire covers fundamentals, deployment, optimization,

security, and real-world scenarios, making it perfect for interview prep or self-study.

1️⃣ Fundamentals

Q1. What is Edge AI and how does it differ from cloud AI?
Answer:
Edge AI runs machine learning models directly on devices (like IoT sensors, cameras, or mobile devices)

rather than relying on cloud servers.

• Benefits: Low latency, reduced bandwidth, better privacy.
  
  

• Cloud AI: Heavy computations done in the cloud; high latency, dependent on internet.
  
  

Q2. Explain the trade-offs between latency, accuracy, and power consumption.
Answer:

• Latency: Time for model inference.
  
  

• Accuracy: How well the model predicts.
  
  

• Power: Energy consumed during inference.
  
  

Optimizing one often impacts others. Example:

• High-accuracy deep networks → higher power and latency.
  
  

• Lightweight models → lower power, faster inference, but possibly lower accuracy.

2️⃣ Hardware & Deployment

Q3. Which hardware accelerators are used for Edge AI?
Answer:

• NVIDIA Jetson Nano/Xavier → GPU-accelerated AI
  
  

• Google Coral TPU → Low-power ML
  
  

• Raspberry Pi with NCS2 → Neural Compute Stick
  
  

Q4. How do you decide what computation happens on the edge vs. cloud?
Answer:

• Edge: Real-time decisions, privacy-sensitive data.
  
  

• Cloud: Heavy training, historical analytics, large datasets.
  
  

Q5. Challenges in deploying ML on low-power IoT devices?
Answer:

• Limited memory and compute.
  
  

• Energy constraints.
  
  

• Real-time requirements.
  
  

• Model updates over-the-air (OTA).
  
  

3️⃣ Software & Optimization

Q6. How do quantization, pruning, and knowledge distillation help Edge AI?

Technique	Description	Benefit
Pruning	Remove less important weights	Reduce model size, energy consumption
Knowledge Distillation	Train small model to mimic large model	Retains performance in lightweight model
Quantization	Reduce precision (float32 → int8)	Smaller model, faster inference

Example: Using TensorFlow Lite for quantization

import tensorflow as tf

# Load a trained model

model = tf.keras.models.load_model('my_model.h5')

# Convert to TensorFlow Lite with quantization

converter = tf.lite.TFLiteConverter.from_keras_model(model)

converter.optimizations = [tf.lite.Optimize.DEFAULT]

tflite_model = converter.convert()

# Save the optimized model

with open('model_quantized.tflite', 'wb') as f:

    f.write(tflite_model)

Q7. How do you handle real-time sensor data processing on edge devices?
Answer:

• Use lightweight frameworks (TensorFlow Lite, ONNX Runtime).
  
  

• Stream data in small batches.
  
  

• Optimize pre-processing for minimal latency.
  
  

Example: Simple real-time sensor processing

import time

import random

def read_sensor():

    return random.random()  # Simulated sensor data

def simple_model(x):

    return x * 2  # Placeholder for ML inference

while True:

    data = read_sensor()

    result = simple_model(data)

    print(f"Sensor: {data:.2f}, Prediction: {result:.2f}")

    time.sleep(0.5)  # Simulate real-time processing

4️⃣ Security & Reliability

Q8. Main security risks in IoT networks running AI?
Answer:

• Data interception (MITM attacks)
  
  

• Malicious firmware updates
  
  

• Model tampering (adversarial attacks)
  
  

Q9. How to safely update ML models over-the-air (OTA)?
Answer:

• Use encrypted channels (TLS)
  
  

• Verify model integrity (hash/signature)
  
  

• Rollback mechanism in case of failures
  

5️⃣ Scenario-Based Questions

Q10. Your camera-based AI device misclassifies objects at night. How do you fix it?
Answer:

• Add night-time training data
  
  

• Use infrared sensors or low-light cameras
  
  

• Adjust image pre-processing (brightness/contrast normalization)
  
  

Q11. A fleet of edge devices generates inconsistent data. How do you debug?
Answer:

• Check sensor calibration
  
  

• Validate model version consistency
  
  

• Implement data logging and replay testing
  
  

• Use OTA updates to patch buggy models
  

6️⃣ Optional Bonus – Practical Coding

Q12. TinyML Example: Deploy a lightweight model on Raspberry Pi (TensorFlow Lite)

import tflite_runtime.interpreter as tflite

import numpy as np

# Load TFLite model

interpreter = tflite.Interpreter(model_path="model_quantized.tflite")

interpreter.allocate_tensors()

input_details = interpreter.get_input_details()

output_details = interpreter.get_output_details()

# Simulated input

input_data = np.array([[0.5]], dtype=np.float32)

interpreter.set_tensor(input_details[0]['index'], input_data)

# Run inference

interpreter.invoke()

output_data = interpreter.get_tensor(output_details[0]['index'])

print("Prediction:", output_data)

✅ This code shows end-to-end inference on a tiny device.

Conclusion

This Edge AI & IoT interview guide combines:

• Theory & fundamentals
  
  

• Real-world scenarios
  
  

• Optimization techniques
  
  

• Security & reliability best practices
  
  

• Code examples
  
  

It’s concise, practical, and blog-ready, perfect for anyone preparing for modern tech interviews or writing a technical article.