AI Engineer Interview Questions for Fresher

 

This collection of 20 AI Engineer interview questions and answers for freshers covers fundamental concepts in AI, machine learning, deep learning, and NLP. Designed for beginners, it explains key topics like supervised vs. unsupervised learning, overfitting, data preprocessing, reinforcement learning, and popular AI tools, helping freshers build confidence for entry-level AI interviews.

Que 1. What is the difference between Artificial Intelligence, Machine Learning, and Deep Learning?

Answer:

  • AI: Broad field of making machines mimic human intelligence.
  • ML: Subset of AI where machines learn from data.
  • Deep Learning: Subset of ML using neural networks with many layers.

Que 2. What are some real-world applications of AI?

Answer:

  • Chatbots and virtual assistants.
  • Recommendation engines (Netflix, Amazon).
  • Fraud detection in banking.
  • Healthcare (disease diagnosis).
  • Autonomous vehicles.

Que 3. What is supervised learning? Give an example.

Answer: Supervised learning uses labeled data for training. Example: Predicting house prices using features like size, location, and number of rooms.

Que 4. What is unsupervised learning? Give an example.

Answer: Unsupervised learning uses unlabeled data to find hidden patterns. Example: Customer segmentation in marketing.

Que 5. What is the difference between classification and regression?

Answer:

  • Classification: Predicts categories (spam or not spam).
  • Regression: Predicts continuous values (temperature, stock prices).

Que 6. What is overfitting in machine learning?

Answer: Overfitting happens when a model memorizes training data but performs poorly on new data. Solution: Use cross-validation, regularization, dropout, or more training data.

Que 7. What is underfitting?

Answer: Underfitting occurs when a model is too simple and cannot capture data patterns. Example: Using a linear model for non-linear data. Solution: Use more complex algorithms or add features.

Que 8. What is a confusion matrix?

Answer: A confusion matrix is used to evaluate classification models.

  • TP (True Positive) — Correctly predicted positive.
  • FP (False Positive) — Wrongly predicted positive.
  • FN (False Negative) — Missed actual positive.
  • TN (True Negative) — Correctly predicted negative.

Que 9. What are activation functions in neural networks?

Answer: Activation functions decide whether a neuron should activate.

  • ReLU: Common for deep learning.
  • Sigmoid: Useful for probabilities.
  • Tanh: Keeps values between -1 and 1.

Que 10. What is the difference between batch gradient descent and stochastic gradient descent?

Answer:

  • Batch GD: Uses the entire dataset for one update.
  • SGD: Updates weights after each sample.
  • Mini-Batch GD: Uses a subset for efficiency and accuracy balance.

Que 11. What is Natural Language Processing (NLP)?

Answer: NLP helps machines understand human language. Examples: Chatbots, Google Translate, sentiment analysis on tweets.

Que 12. What is the difference between structured and unstructured data?

Answer:

  • Structured Data: Organized into rows and columns (e.g., databases).
  • Unstructured Data: Raw data like text, images, audio, and video.

Que 13. What is feature engineering?

Answer: Feature engineering means creating or modifying features to improve model accuracy. Example: From “Date of Birth,” creating “Age” as a new feature.

Que 14. What is data preprocessing and why is it important?

Answer: Data preprocessing prepares raw data for models.

  • Handle missing values.
  • Normalize or standardize.
  • Encode categorical variables.
  • Remove duplicates and outliers. It ensures higher accuracy and better performance.

Que 15. What is reinforcement learning in simple words?

Answer: Reinforcement Learning is learning by trial and error. An agent interacts with the environment, receives rewards for good actions, and penalties for bad ones. Example: Training a robot to walk.

Que 16. What are embeddings in NLP?

Answer: Embeddings are vector representations of words.

  • Similar words are closer in vector space.
  • Examples: Word2Vec, GloVe, BERT embeddings.
  • Useful in machine translation, chatbots, and search engines.

Que 17. What are some common challenges in AI projects?

Answer:

  • Lack of clean and sufficient data.
  • Overfitting/underfitting.
  • High computational cost.
  • Model interpretability.
  • Deployment and monitoring issues.

Que 18. How do you handle missing data?

Answer:

  • Remove rows (if small percentage missing).
  • Fill with mean, median, or mode.
  • Advanced methods: kNN, regression imputation.
  • Treat missing values as a separate feature.

Que 19. What is the difference between classification and clustering?

Answer:

  • Classification: Supervised, predicts categories (fraud or not fraud).
  • Clustering: Unsupervised, groups similar data (customer segmentation).

Que 20. Name some popular AI frameworks and libraries.

Answer:

  • Machine Learning: Scikit-learn, XGBoost.
  • Deep Learning: TensorFlow, PyTorch, Keras.
  • NLP: NLTK, spaCy, Hugging Face Transformers.

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AI Engineer Interview Questions for Fresher

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