300 PRACTICE QUESTIONS

AI & Machine Learning Mock Interview Questions & Practice

Prepare for AI & Machine Learning placement and job interviews with 300 technical practice questions across 10 subjects: Natural Language Processing, Computer Vision & Reinforcement Learning, Data Structures & Algorithms, Operating Systems, Database Management Systems, Computer Networks, Object-Oriented Programming, Machine Learning Fundamentals, Deep Learning & Neural Networks and Statistics & Probability for Data Science. Below are sample AI & Machine Learning interview questions with the key points a strong answer covers. Practice them out loud in a scored mock interview — your first session is free.

Subjects covered in the AI & Machine Learning mock interview

Natural Language Processing

  • NLP Fundamentals & Text Preprocessing
  • Language Models & Word Embeddings
  • NLP Applications

Computer Vision & Reinforcement Learning

  • Image Processing & Feature Extraction
  • Object Detection & Image Classification
  • Reinforcement Learning Fundamentals

Data Structures & Algorithms

Shared with Computer Science Engineering

  • Arrays & Strings
  • Linked Lists & Trees
  • Sorting, Searching & Complexity

Operating Systems

Shared with Computer Science Engineering

  • Process & Thread Management
  • Memory Management
  • Concurrency & Synchronization

Database Management Systems

Shared with Computer Science Engineering

  • Relational Model & SQL
  • Normalization & Schema Design
  • Transactions & Indexing

Computer Networks

Shared with Computer Science Engineering

  • Networking Fundamentals & OSI Model
  • TCP/IP & Routing
  • Application Layer Protocols & Security

Object-Oriented Programming

Shared with Computer Science Engineering

  • Core OOP Principles
  • Design Patterns
  • Inheritance, Polymorphism & SOLID

Machine Learning Fundamentals

Shared with AI & Data Science

  • Supervised Learning Basics
  • Unsupervised Learning & Clustering
  • Model Evaluation & Feature Engineering

Deep Learning & Neural Networks

Shared with AI & Data Science

  • Neural Network Fundamentals
  • Convolutional Neural Networks
  • Recurrent Networks & Sequence Models

Statistics & Probability for Data Science

Shared with AI & Data Science

  • Probability Fundamentals
  • Descriptive Statistics & Distributions
  • Statistical Inference & Hypothesis Testing

Sample AI & Machine Learning interview questions

A selection from the AI & Machine Learning question bank. In a MockManthan session the interviewer asks each question aloud and you answer on camera — these key points show what a complete answer should include.

Natural Language Processing interview questions

  1. What is the difference between stemming and lemmatization?

    NLP Fundamentals & Text Preprocessing

    Key points to cover:

    • Stemming: simple, rule-based suffix removal, can produce non-words
    • Lemmatization: uses vocabulary and morphological analysis
    • Produces valid dictionary base forms (lemmas)
  2. What is Word2Vec, and what is its basic training approach?

    Language Models & Word Embeddings

    Key points to cover:

    • Learns word embeddings via a shallow neural network
    • Predicts a word from its context (or vice versa)
    • Words appearing in similar contexts get similar vectors
  3. What is text summarization, and what is the difference between extractive and abstractive summarization?

    NLP Applications

    Key points to cover:

    • Producing a shorter version of a text retaining key information
    • Extractive: selects and combines existing sentences from the source
    • Abstractive: generates new sentences not verbatim in the source

Computer Vision & Reinforcement Learning interview questions

  1. What is edge detection in image processing, and what does it aim to identify?

    Image Processing & Feature Extraction

    Key points to cover:

    • Identifies points where brightness changes sharply
    • Typically corresponding to object boundaries
    • A foundational step for further image analysis
  2. What is Intersection over Union (IoU), and how is it used to evaluate object detection performance?

    Object Detection & Image Classification

    Key points to cover:

    • Measures overlap between predicted and ground truth bounding boxes
    • Area of overlap divided by area of union
    • Determines whether a detection is considered correct
  3. What is the difference between exploration and exploitation in reinforcement learning?

    Reinforcement Learning Fundamentals

    Key points to cover:

    • Exploration: trying new, potentially suboptimal actions
    • To discover outcomes and gather information
    • Exploitation: choosing actions believed to yield the highest reward

Data Structures & Algorithms interview questions

  1. Explain how you would detect if two strings are anagrams of each other.

    Arrays & Strings

    Key points to cover:

    • Character frequency count comparison
    • Sorting approach as an alternative, O(n log n)
    • Discusses the time/space trade-off between the two approaches
  2. How would you find the lowest common ancestor of two nodes in a binary search tree?

    Linked Lists & Trees

    Key points to cover:

    • Uses the BST property while traversing from the root
    • States O(h) time complexity
    • Contrasts with general binary-tree LCA needing a full traversal
  3. How does quicksort's pivot selection affect its performance?

    Sorting, Searching & Complexity

    Key points to cover:

    • Poor pivot choice (e.g. first element on sorted input) degrades to O(n^2)
    • Randomized or median-of-three pivot selection helps avoid the worst case
    • Connects pivot quality to partition balance

Operating Systems interview questions

  1. Explain the difference between multithreading and multiprocessing.

    Process & Thread Management

    Key points to cover:

    • Multithreading shares memory within one process, lower overhead
    • Needs synchronization to avoid shared-state issues
    • Multiprocessing uses separate memory spaces, higher isolation and overhead
  2. Compare paging and segmentation as memory management schemes.

    Memory Management

    Key points to cover:

    • Paging uses fixed-size units, transparent to the programmer
    • Segmentation uses variable-size logical units (code/data/stack)
    • Notes segmentation can suffer external fragmentation
  3. How does a condition variable work, and what problem does it solve?

    Concurrency & Synchronization

    Key points to cover:

    • Lets a thread wait/block until a specific condition becomes true
    • Signaled by another thread
    • Solves busy-waiting inefficiency compared to polling a flag

Database Management Systems interview questions

  1. What is a subquery, and give an example of when you'd use one.

    Relational Model & SQL

    Key points to cover:

    • A query nested inside another query
    • Useful for filtering based on an aggregate or related table result
    • Gives a concrete example (e.g. rows above a subquery-computed average)
  2. Explain Third Normal Form (3NF) and what problem it solves.

    Normalization & Schema Design

    Key points to cover:

    • 2NF plus no transitive dependency
    • Non-key columns must depend only on the primary key
    • Removes redundancy caused by indirect (transitive) dependencies
  3. Explain the difference between the READ COMMITTED and REPEATABLE READ isolation levels.

    Transactions & Indexing

    Key points to cover:

    • READ COMMITTED prevents dirty reads
    • READ COMMITTED still allows non-repeatable reads within a transaction
    • REPEATABLE READ additionally prevents non-repeatable reads

Computer Networks interview questions

  1. What is the role of the Network layer, and what protocol is most associated with it?

    Networking Fundamentals & OSI Model

    Key points to cover:

    • Handles logical addressing
    • Handles routing of packets between networks
    • Associates it with IP (Internet Protocol)
  2. What is the purpose of TCP's flow control, and how is it achieved?

    TCP/IP & Routing

    Key points to cover:

    • Prevents a fast sender from overwhelming a slow receiver
    • Achieved via a sliding window mechanism
    • Receiver advertises how much data it can currently accept
  3. What is the purpose of TLS/SSL in network security?

    Application Layer Protocols & Security

    Key points to cover:

    • Provides encryption (confidentiality)
    • Provides integrity
    • Provides authentication of the server (and optionally client)

How to practice AI & Machine Learning interview questions on MockManthan

  1. Create a free account and complete a quick camera, microphone and face calibration.
  2. Pick the AI & Machine Learning subject, chapter and topic you want to practice.
  3. Answer each question out loud to the animated interviewer, then review your scored report — Confidence, Professionalism, Eye Contact and Body Language — with a clickable video timeline.

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