AI Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

1. Why AI Interview Preparation Matters

Before we dive into the questions themselves, it’s important to understand why structured interview prep is so essential. AI roles often require a blend of skills across computer science, mathematics, and practical engineering. Employers are seeking candidates who can code with elegance, efficiently handle data, and design scalable architectures for advanced AI solutions.

1. Demonstrate Your Problem-Solving Skills:
   Many AI positions revolve around solving novel or non-trivial problems. Coding challenges and system design tasks let you show how you approach new and complex tasks—how you break them down, reason about them, and find workable solutions.

2. Showcase Your Breadth of Knowledge:
   The field of AI can encompass everything from data wrangling to neural network architecture. Technical interviewers will often probe your versatility with concepts across statistics, algorithms, design patterns, and more.

3. Manage Your Stress Levels:
   Interviews can be stressful, but the more prepared you are, the more comfortable you’ll feel. Knowing typical question patterns helps you remain calm under time constraints, ensuring you can think clearly and logically.

4. Stand Out to Employers:
   Because AI jobs are so in-demand, having a solid command over fundamental coding and system design topics can put you in the top tier of applicants. Structured preparation helps you perform confidently and memorably.

By focusing on both coding and system design, you’ll ensure you’re not just superficially prepared—but that you have the depth and range necessary to handle real-world AI challenges.

2. 15 Real Coding Interview Questions

Below are 15 coding interview questions often asked in the context of AI or data-focused roles. Use these prompts to practise writing efficient, optimised solutions. We’ve also included hints at potential solution strategies.

Coding Question 1: Two-Sum Variant for Large Arrays

Question: Given an array of integers and a target sum, return the indices of two numbers that add up to the target. Ensure that your solution scales for large arrays typical in AI data pipelines.
What to focus on:

• Time complexity (O(n) approach using a hash map).

• Edge cases and handling duplicates.

Coding Question 2: Merging K Sorted Lists

Question: Merge k sorted linked lists into one sorted linked list and return it. This is common in scenarios where AI pipelines produce sorted data outputs in parallel.
What to focus on:

• Use of a min-heap (priority queue) for efficient merging.

• Typical time complexity of O(n log k).

Coding Question 3: Longest Increasing Subsequence

Question: Find the length of the longest strictly increasing subsequence in an array.
What to focus on:

• Dynamic programming (O(n²)) or a more advanced O(n log n) solution using a sorted structure.

• ML context: identifying patterns in training sequences.

Coding Question 4: Reverse Nodes in k-Group

Question: Given a linked list, reverse the nodes of the list k at a time and return the modified list.
What to focus on:

• Pointer manipulation.

• Edge cases (like partial groups at the end).

Coding Question 5: Maximal Rectangle in a Binary Matrix

Question: Find the largest rectangular area of 1s in a 2D binary matrix.
What to focus on:

• Histogram-based algorithms for O(n*m) solutions.

• Application in image-based AI tasks (segmenting image regions).

Coding Question 6: Subarray Sum Equals K

Question: In an array, find the number of continuous subarrays whose sum equals a given value k.
What to focus on:

• Cumulative sums and a hash map (O(n) approach).

• Useful in AI for rolling windows in time-series predictions.

Coding Question 7: Rotate a Matrix

Question: Given a matrix (n x n), rotate it 90 degrees in place.
What to focus on:

• In-place transformation logic.

• Relevant to image transformations in computer vision roles.

Coding Question 8: Word Break Problem

Question: Given a dictionary of words and a target string, determine if the target string can be segmented into the dictionary words.
What to focus on:

• Dynamic programming approach for string segmentation.

• NLP relevance: tokenisation and phrase identification.

Coding Question 9: K-th Largest Element in a Stream

Question: Implement a class to find the k-th largest element in a running stream of numbers.
What to focus on:

• Min-heap (size k) for efficient updates.

• Big data stream handling, crucial in real-time AI analytics.

Coding Question 10: Trie Implementation

Question: Implement a trie (prefix tree) with the ability to insert a word and search for a word.
What to focus on:

• Efficient prefix-based data structure for NLP tasks.

• Handling edge cases (e.g., empty strings or repeated words).

Coding Question 11: Find All Anagrams in a String

Question: Return the starting indices of anagrams of a particular word within a given string.
What to focus on:

• Sliding window approach with frequency maps.

• Hashing for quick comparison of character counts.

Coding Question 12: Detect Cycle in a Linked List

Question: Determine if a linked list contains a cycle and, if so, return the node where the cycle begins.
What to focus on:

• Floyd’s Tortoise and Hare algorithm (O(n) time, O(1) space).

• Data structure fundamentals tested in many interviews.

Coding Question 13: Flatten a Multi-Level Doubly Linked List

Question: Flatten a doubly linked list where nodes can have a child pointer, merging all nodes into a single-level doubly linked list.
What to focus on:

• Recursion or iterative stack-based approach.

• Pointer manipulation and thorough handling of edge cases.

Coding Question 14: LRU Cache Implementation

Question: Design and implement an LRU (Least Recently Used) cache.
What to focus on:

• Double-linked list + hash map combination.

• Caching is critical for optimising AI model deployments.

Coding Question 15: Binary Tree Right Side View

Question: Given a binary tree, return the values of the nodes you can see ordered from top to bottom when you stand to the right side of it.
What to focus on:

• BFS or DFS with level tracking.

• Extendable concept for 3D point cloud analytics.

When practising these coding problems, pay attention to:

• Time complexity: Aim for the most efficient approach without overcomplicating the solution.

• Space complexity: Optimise memory usage, critical when handling large datasets.

• Code readability: Interviewers often prefer clear, well-structured code that’s easy to explain.

3. 15 System Design & Architecture Questions

AI interviews often involve system design sessions where you’ll need to conceptualise entire architectures. This section includes 15 common system design and architecture questions that relate to AI pipelines, data processing, and model deployment. Understanding these concepts will enable you to articulate how you’d build scalable solutions and integrate them into production environments.

System Design Question 1: Building a Recommendation Engine

Scenario: Design a system that generates movie recommendations for millions of users based on their watch history and ratings.
Key Points to Discuss:

• Data ingestion (user-watch events).

• Collaborative filtering vs. content-based filtering.

• Real-time vs. batch predictions.

• Scalable storage solutions (NoSQL databases, distributed file systems).

System Design Question 2: Real-Time Fraud Detection

Scenario: You need to design a system that detects fraudulent transactions in real-time for an e-commerce platform.
Key Points to Discuss:

• Streaming data pipelines (Kafka, AWS Kinesis).

• Low-latency model serving (online inference).

• Real-time feature engineering.

• Model updates to adapt to emerging fraud patterns.

System Design Question 3: Chatbot Platform for Customer Support

Scenario: Plan the architecture for an intelligent chatbot used by a bank for customer support queries.
Key Points to Discuss:

• NLP components (intent detection, entity recognition).

• Dialogue management (state machine, context tracking).

• Backend integration (secure communication with banking systems).

• Scalability and fallback to human agents.

System Design Question 4: Image Classification at Scale

Scenario: Design a pipeline to classify millions of images daily for an online gallery.
Key Points to Discuss:

• Data ingestion and preprocessing (resizing, normalising).

• Model selection (CNNs, Vision Transformers).

• Hardware acceleration (GPUs, TPUs for inference).

• Caching & load balancing for large-scale requests.

System Design Question 5: Personalised News Feed

Scenario: Build a personalised news feed system that curates articles for users based on reading history and preferences.
Key Points to Discuss:

• Data collection (clickstream, user profile).

• Recommendation algorithm (hybrid filtering: content + user data).

• Ranking and scoring (relevance, recency).

• Architecture scaling for millions of users.

System Design Question 6: Monitoring & Logging for AI Pipelines

Scenario: Develop a logging and monitoring system for complex AI pipelines running 24/7.
Key Points to Discuss:

• Centralised logging (ELK stack).

• Monitoring tools (Prometheus, Grafana).

• Alerting mechanisms (metrics-based alerting, anomaly detection).

• Scalability for high-throughput data flows.

System Design Question 7: Document Search Engine

Scenario: Build a system that indexes thousands of documents daily and allows users to perform keyword-based and semantic search.
Key Points to Discuss:

• Indexing pipeline (inverted indices, vector embeddings).

• Search algorithms (BM25, semantic search with BERT).

• Near real-time updates for continuous indexing.

• Load balancing for large query volumes.

System Design Question 8: Speech Recognition Service

Scenario: Design a speech-to-text service for multiple languages with high concurrency demands.
Key Points to Discuss:

• Streaming recognition (audio processed in chunks).

• Large vocabulary handling.

• Model selection (RNN, Transformer-based).

• Latency reduction (model quantisation, distributed inference).

System Design Question 9: Scaling a Data Labelling Platform

Scenario: You need to build a data labelling system for an AI company that depends on manual labellers and automatic suggestions.
Key Points to Discuss:

• User-friendly labelling interface.

• Queue mechanisms (assign tasks to labellers).

• Automated pre-labelling (model inference).

• Version control for datasets.

System Design Question 10: A/B Testing Infrastructure

Scenario: Construct an infrastructure to run A/B tests on AI model variants in production.
Key Points to Discuss:

• Routing traffic to different model variants.

• Metrics collection (user behaviour, model accuracy).

• Statistical significance checks.

• Continuous integration/continuous deployment (CI/CD) for models.

System Design Question 11: Recommender System for Job Listings

Scenario: You’re tasked with designing a recommendation engine that matches job seekers to job listings—akin to www.artificialintelligencejobs.co.uk.
Key Points to Discuss:

• Feature engineering (skills, location, experience).

• Multimodal data (text from CVs, structured data).

• Candidate ranking (matching algorithms, semantic similarity).

• Handling user feedback (reinforcement signals to refine recommendations).

System Design Question 12: Distributed Training for Deep Neural Networks

Scenario: Set up an environment where large deep learning models can be trained on multiple GPUs across multiple machines.
Key Points to Discuss:

• Data parallel vs. model parallel.

• Synchronisation (Parameter Server, AllReduce).

• Frameworks (TensorFlow, PyTorch).

• Fault tolerance (checkpointing, node failures).

System Design Question 13: Edge AI for Autonomous Vehicles

Scenario: Design the system responsible for real-time inference in autonomous vehicles.
Key Points to Discuss:

• On-board hardware constraints (limited GPU/TPU).

• Low-latency requirements for real-time decision-making.

• Sensor fusion (Lidar, Radar, Cameras).

• Handling intermittent connectivity and remote updates.

System Design Question 14: Time-Series Forecasting Platform

Scenario: Build a platform to forecast time-series data for energy consumption on a national scale.
Key Points to Discuss:

• Data ingestion (real-time sensor streams).

• Model selection (ARIMA, LSTM, Transformer-based).

• Scalability for large-scale data.

• Error monitoring (MAE, RMSE).

System Design Question 15: Privacy-Preserving Machine Learning System

Scenario: Implement a system where user data must remain confidential, but you still need to train AI models effectively.
Key Points to Discuss:

• Federated learning vs. centralised approaches.

• Differential privacy (ensuring no single data point is identifiable).

• Encrypted computation (homomorphic encryption, secure multiparty).

• GDPR compliance for data privacy in UK/EU.

In system design interviews, focus on explaining why you make certain choices, not just what choices you make. Discuss trade-offs around performance, cost, scalability, and complexity. Show awareness of real-world constraints such as memory limits, network bottlenecks, and hardware availability.

4. Tips for Conquering AI Job Interviews

Mastering coding and system design questions is just one aspect of succeeding in AI interviews. Below are some top tips to help you perform at your best and stand out from the crowd.

1. Brush Up on Core Algorithms & Data Structures

   • Sorting, searching, hashing, and dynamic programming are staples.

   • For ML roles, you may also be quizzed on algorithms like gradient descent or matrix decompositions.

2. Understand Machine Learning Foundations

   • Even if the coding question seems purely algorithmic, be prepared to connect it to ML concepts.

   • Know cost functions, optimisers, regularisation techniques, and model evaluation metrics.

3. Practice Whiteboard & Collaborative Coding

   • AI interviews often involve a whiteboard or shared online editor—get comfortable with minimal debugging aids.

   • Think aloud, explain your approach, and keep your code readable.

4. Learn to Communicate Effectively

   • System design interviews focus on how you articulate complex ideas and architecture trade-offs.

   • Draw high-level diagrams and identify bottlenecks or failovers.

5. Time Management

   • Coding interviews usually come with strict time limits—plan your approach quickly.

   • If stuck, outline your logic and partial solutions rather than staying silent.

6. Stay Calm Under Pressure

   • Interviewers want to see how you handle unexpected hurdles.

   • A composed mindset can help you think more clearly.

7. Be Aware of Industry Trends

   • Familiarise yourself with advanced AI topics like large language models, MLOps, or neural architecture search.

   • Tailor your knowledge to the company’s domain—vision tasks, NLP, robotics, etc.

8. Ask Questions

   • Clarify requirements to ensure you understand the problem fully.

   • In system design interviews, uncover ambiguous constraints to show your product thinking.

9. Rehearse with Mock Interviews

   • Pair with peers or use virtual platforms.

   • Watch recordings of yourself to identify areas of improvement, both technically and in communication.

10. Customise Your Solutions

• If it’s a data-heavy role, highlight your data engineering, ETL, and feature engineering skills.

• If it’s deep learning, emphasise experience with distributed training, model tuning, and GPU/TPU usage.

With strong technical skills, solid communication, and a good grasp of industry developments, you’ll be well on your way to acing your AI interviews.

5. Final Thoughts

Coding and system design questions form the backbone of most AI job interviews. By practising the 30 real questions listed in this guide, you’re preparing yourself to tackle a wide range of problems—from array manipulation and dynamic programming to the scalable design of recommendation engines and AI-driven fraud detection.

Remember, interviews are a two-way process. While they test your technical acumen, it’s also your opportunity to evaluate how well the organisation’s goals align with your own career ambitions in AI. Always research the company’s AI domain—whether it’s computer vision, NLP, or robotics—and tailor your expertise accordingly.

If you’re on the hunt for exciting AI opportunities, www.artificialintelligencejobs.co.uk is an excellent resource. You’ll find cutting-edge AI roles across various industries, all in one place. Armed with these interview questions and a clear study strategy, you’ll be ready to stand out from the crowd and secure that dream AI position in the UK.

Good luck, and here’s to your continued success in the ever-evolving world of Artificial Intelligence!