Artificial Intelligence (AI) is rapidly transforming how industries function, making it a hot career path for engineers, data scientists, and developers. Whether you are aiming for a role in machine learning, deep learning, or general AI, preparing for interviews with a solid grasp of core and advanced concepts is key. This comprehensive guide explores 50 of the most commonly asked Artificial Intelligence interview questions, grouped by difficulty level: Basic, Intermediate, and Advanced. Each answer includes practical context, work-domain relevance, and Python code where useful to bring concepts to life.
Basic Level AI Interview Questions
1. What is Artificial Intelligence?
AI is the field of computer science focused on building smart machines capable of performing tasks that typically require human intelligence. Examples include voice recognition in Alexa, recommendation systems in Netflix, or autonomous vehicles.
2. How is Artificial Intelligence different from Machine Learning?
Artificial Intelligence is the broader discipline. Machine Learning (ML) is a subset of AI that trains algorithms to learn from data. Think of AI as the system and ML as the engine driving it. For example, Gmail’s spam filter is ML in action under the AI umbrella.
3. What are the types of AI?
Narrow Artificial Intelligence: Designed for specific tasks (e.g., facial recognition)
General Artificial Intelligence: Human-level cognitive function (still theoretical)
Super Artificial Intelligence: Surpasses human intelligence (futuristic concept)
4. Define Machine Learning.
Machine Learning is about building models that learn from past data to make predictions or decisions. In healthcare, ML helps predict patient readmission rates based on historical records.
5. What are supervised, unsupervised, and reinforcement learning?
Supervised: Uses labeled data. E.g., predicting house prices using square footage and location.
Unsupervised: Unlabeled data. E.g., customer segmentation in marketing.
Reinforcement: Agents learn through trial-and-error using rewards. E.g., self-driving cars learning to park.
6. What is the Turing Test?
A test proposed by Alan Turing to assess if a machine can exhibit behavior identical from a human. ChatGPT-like models are inching toward this goal.
7. Give the name of some Real-world AI applications:
Voice assistants like Siri
E-commerce personalization
Fraud detection in banking
Automated resume screening in HR
8. What is deep learning?
Deep Learning is a subset of ML using neural networks with many layers to model complex patterns. In image classification, CNNs (a deep learning model) can distinguish cats from dogs.
9. Difference between AI and Deep Learning?
AI is the overarching concept; Deep Learning is one method to achieve AI using neural nets. For example, AI might power a chatbot, while Deep Learning trains it to understand human context.
10. What is overfitting?
A model performs great on training data but poorly on new data because it “memorized” rather than generalized.
from sklearn.linear_model import LinearRegression
model = LinearRegression().fit(X_train, y_train)
print(model.score(X_train, y_train)) # 0.99
print(model.score(X_test, y_test)) # 0.52 # Overfitting
Intermediate Level AI Interview Questions
11. What is underfitting?
Occurs when a model is too simple to capture data trends. For example, using linear regression on non-linear data.
12. What are precision and recall?
Precision: Out of all predicted positives, how many were correct.
Recall: Out of all actual positives, how many did we catch.
- Precision ensures reliability of positive predictions.
Recall ensures completeness in capturing actual positives.
from sklearn.metrics import precision_score, recall_score
precision_score(y_true, y_pred)
recall_score(y_true, y_pred)13. What is a confusion matrix?
A confusion matrix is a table used to evaluate the performance of a classification model. It compares the actual labels with the predicted labels and helps you understand how well your model is performing—especially in multi-class or binary classification problems.
Key Terms:
True Positive (TP): Model predicted positive, and it was actually positive.
False Positive (FP): Model predicted positive, but it was actually negative (also called Type I error).
True Negative (TN): Model predicted negative, and it was actually negative.
False Negative (FN): Model predicted negative, but it was actually positive (also called Type II error).
14. Key steps to build an ML model:
Data Collection (e.g., CSVs, databases)
Preprocessing (handling nulls, scaling)
Feature Engineering
Model Selection and Training
Evaluation using test data
Deployment using Flask or FastAPI
15. Explain bias-variance tradeoff.
High bias = underfitting (model too simple)
High variance = overfitting (model too complex) The goal is to strike a balance.
16. What is gradient descent?
An optimization algorithm used to minimize the cost function in training.
def gradient_descent(w, X, y, lr):
for epoch in range(100):
grad = compute_gradient(w, X, y)
w = w - lr * grad17. Batch vs Mini-batch vs Stochastic Gradient Descent:
The difference between Batch, Mini-batch, and Stochastic Gradient Descent (SGD) lies in how much data is used to compute the gradient and update the model during training.
All three are optimization techniques based on gradient descent, used to minimize a model’s loss function.
1. Batch Gradient Descent
It Uses all training data to compute the gradient and update the weights once per epoch.
Update Frequency: One update per epoch.
Pros:
Stable and accurate gradients.
Converges smoothly.
Cons:
Slow and memory-intensive for large datasets. Not suitable for online or streaming data.
Example:
If you have 10,000 samples, it processes all 10,000 to make one weight update.
2. Stochastic Gradient Descent (SGD)
Uses only one training example to compute the gradient and update the weights after every sample.
Update Frequency: One update per sample.
Pros:
Fast and memory-efficient. Can escape local minima due to noisy updates.
Cons:
High variance in updates can cause the loss to fluctuate heavily. May struggle to converge smoothly.
Example:
If you have 10,000 samples, it makes 10,000 weight updates per epoch.
3. Mini-batch Gradient Descent
Uses a subset of training data (mini-batch) to compute the gradient and update the weights.
Update Frequency: One update per mini-batch (e.g., batch size = 32, 64, 128, etc.).
Pros:
Balances stability and speed. Allows use of vectorized operations (faster on GPUs). Reduces memory usage compared to full batch.
Cons:
Still some noise in updates. Requires tuning batch size.
Example:
If you have 10,000 samples and a mini-batch size of 100, you’ll get 100 updates per epoch.
Mini-batch gradient descent is the most commonly used method in deep learning because it offers a good balance between computational efficiency and convergence performance.
18. What is backpropagation?
Algorithm for training neural networks by calculating the gradient of the loss function and updating weights accordingly.
19. How does a decision tree work?
It recursively splits data based on features to form branches leading to decision outcomes.
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier().fit(X, y)20. What is entropy in decision trees?
It measures randomness. Lower entropy = more pure. Used to choose the best feature to split on.
21. What is regularization?
It adds a penalty term to the loss to discourage overfitting:
L1 = Lasso (sparse weights)
L2 = Ridge (small weights)
22. What is cross-validation?
A technique to validate model performance across different data splits.
from sklearn.model_selection import cross_val_score
cross_val_score(model, X, y, cv=5)23. Handling imbalanced datasets:
Oversample minority class
Undersample majority
Use SMOTE
Choose AUC over accuracy
24. What is a ROC curve?
A graph of True Positive Rate vs False Positive Rate. The closer the AUC to 1, the better.
25. What are word embeddings?
Represent words as vectors to capture meaning. Word2Vec and GloVe are popular methods.
from gensim.models import Word2Vec
model = Word2Vec(sentences, vector_size=100)
vector = model.wv['apple']Advanced Level AI Interview Questions
26. Explain CNN architecture:
CNN stands for Convolutional Neural Network. It is a type of Deep Learning. It is used for Image data processing.
We can divide CNN architecture in 6 group:-
i. Input Layer: Take raw image input data in 2D form or 3D form.
ii. Convolutional Layers: Apply filters (kernels) that slide over the input to detect features like edges, textures, or shapes.
Extract Features: Each filter generates a feature map.
iii. Activation Function (usually ReLU)
Helps the network learn complex patterns.
Introduces non-linearity to the model
iv. Pooling Layers (e.g., Max Pooling)
Downsample the feature maps.
Reduce spatial size and computational load.
Preserve important features while discarding noise.
v. Fully Connected Layers
Flatten the pooled feature maps into a 1D vector.
Pass through dense layers for final classification or regression.
vi. Output Layer
Produces the final prediction (e.g., softmax for classification).
27. What is an RNN?
Recurrent Neural Networks handle sequences. Used in stock prediction or language translation.
28. Vanishing/Exploding Gradients:
Common in deep networks. Gradients become too small (vanishing) or too large (exploding), making training unstable.
29. How LSTM solves this?
It uses gates (input, output, forget) to maintain long-term memory.
30. What is the Transformer?
A deep learning architecture using self-attention. Models like BERT and GPT are built on it.
31. What is attention in DL?
Mechanism to weigh the importance of each word or element. Crucial in translation tasks.
32. What is reinforcement learning?
Agents learn actions by maximizing cumulative rewards in an environment. Example: Training a drone to navigate obstacles.
33. Q-learning explained:
A type of RL algorithm that updates Q-values using Bellman equation.
Q[state][action] = reward + gamma * max(Q[next_state])34. What is an MDP?
Markov Decision Process defines states, actions, transition probabilities, and rewards.
35. What is transfer learning?
Using pre-trained models on a new problem. Example: Use ImageNet-trained ResNet for X-ray classification.
36. What are GANs?
Two networks compete:
Generator: Produces fake data
Discriminator: Detects real vs fake Used in face generation, art, and data augmentation.
37. How to deploy ML models?
Serialize model using joblib/pickle
Create API (Flask)
Deploy via Docker or to cloud (AWS/GCP)
38. What is model drift?
Model performance drops as new data distribution changes. Requires retraining.
39. Explainable AI (XAI):
Helps understand model predictions. Tools: SHAP, LIME.
40. How does BERT work?
BERT reads text bidirectionally, allowing it to understand full context. Pre-trained on masked language models.
41. NLP Challenges:
Ambiguity in language
Sarcasm detection
Domain-specific terms
Multilingual content
42. What is zero-shot learning?
Model generalizes to unseen classes using semantic descriptors. E.g., classifying a “koala” with no training images.
43. Evaluating clustering:
Silhouette Score
Davies-Bouldin Index
Visualize with t-SNE or PCA
44. What are autoencoders?
Unsupervised networks to learn efficient data representations. Used in anomaly detection or image denoising.
45. What is the role of Activation functions in model training?
Introduce non-linearity. Examples: ReLU, sigmoid, tanh.
import torch.nn.functional as F
output = F.relu(input_tensor)46. How you can differentiate between Parameter and Hyperparameter in AI models?
Parameters: Learned (weights, biases)- This is the result of after training models. Parameters like key performance indicators(KPIs) after learning models.
Hyperparameters: Pre-set (learning rate, epochs) – These are being set by mostly developers before training the models.
47. How to Schedule Learning rate in Deep Neural Network Models?
We can adjusts learning rate dynamically to improve convergence. This will reduce manual processing and reduce human effort.
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.5)48. How you can Monitoring models in production?
We can check some basic parameters, such as:
Log performance metrics
Watch for data drift
Alert on anomalies
49. What are some ethical concerns of AI?
Bias in training data- Bias in training data may lead to miscalculations in decision making. For largecap size companies such biases can cost a lot.
Data privacy: We leak data on social media platforms or any websites intentionally and unintentinally as well. Data leaking can be by many means, like sharing personal data during sign up, sharing picture, text, commenting and many others. There are not a common rule worldwide. Some countries like india has formed Data privacy rules. But such rules are restricted within the boundary of India. ANd there is not stringent punishment available.
Explainability
Job displacement
50. What is the recent AI trends?
SInce their are rapid development and every day launch of new tech solutions in AI. But broadly we can club these developments in some of the groups such as :
Generative AI tools
AI in cybersecurity
Edge AI (on-device ML)
AI regulation (EU AI Act)
Final Thoughts
These 50 questions cover everything from foundational concepts to advanced applications in AI. But memorizing answers isn’t enough practice coding, solve problems on Kaggle, and build real-world projects. That’s how you’ll stand out.
Next Steps:
Build mini-projects
Read AI research papers
Mock interviews with peers
Stay updated with AI news
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