← Majid Al Futtaim Interview Insights
Part of the OA batch so no real discussion happened here, just coded it up and moved on.
Use a stack to simulate asteroid collisions: iterate through the array, and for each asteroid, resolve collisions with the top of the stack until a stable state is reached. Only right-moving asteroids (positive) can collide with left-moving ones (negative) that come after them, so the stack naturally handles the order.
Pro tip: Clarify the collision rules upfront (same direction never collide, equal size annihilate both) and mention edge cases like empty input or all asteroids moving the same direction. This shows attention to detail and prevents misunderstandings.
Restate the collision rules: asteroids move in their direction, collisions occur only when a right-moving asteroid meets a left-moving one, and equal sizes destroy both. Confirm with the interviewer if needed.
Recognize that a stack is ideal because collisions happen between the most recent surviving asteroid and the current one, following a last-in-first-out order.
For each asteroid, while the stack is not empty and the top is positive and the current is negative, compare absolute sizes. Pop the top if it's smaller, skip the current if it's smaller, or pop and skip if equal.
After resolving all possible collisions, push the current asteroid onto the stack if it hasn't been destroyed.
Convert the stack to an array and return it as the result, ensuring the order is preserved.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This one's the tree version where you can't rob adjacent nodes.
Use a post-order DFS that returns two values per node: the maximum loot if the node is robbed and if it is not. Combine child results bottom-up: if robbed, add the 'not robbed' values of children; if not robbed, take the max of each child's two states. Return the max of the root's two states.
Pro tip: Explicitly state the recurrence and its O(n) time/O(h) space complexity, then mention that a naive top-down memoization on (node, parentRobbed) also works but the two-value return is cleaner and avoids a hash map.
Restate the rules: cannot rob a node and its direct child; maximize total money. Ask about tree size, value ranges, and whether the tree can be empty or skewed.
For each node, define two values: rob[node] = max money if this node is robbed; skip[node] = max money if this node is not robbed.
rob[node] = node.val + skip[left] + skip[right]; skip[node] = max(rob[left], skip[left]) + max(rob[right], skip[right]).
Recursively compute the pair for left and right subtrees, then combine at the current node. Return the pair up the call stack.
At the root, return max(rob[root], skip[root]). State time complexity O(n) and space complexity O(h) for recursion stack.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Trickiest of the three OA problems for me.
Clarify the definition of a valid BST subtree, then propose a post-order traversal that returns for each node whether its subtree is a BST, along with the min and max values and the count of valid BST subtrees. Use this information to count valid BST subtrees in O(n) time and O(h) space.
Pro tip: Mention that a single node is always a valid BST, and that you can avoid extra space by returning a tuple (isBST, min, max, count) from the recursive function. Also, discuss how to handle duplicate values if the BST definition allows them.
Ask whether a valid BST subtree means every node in the subtree satisfies the BST property relative to the subtree's root, and whether duplicate values are allowed. Confirm that a single node counts as a valid BST.
Use post-order traversal because subtree information is needed before processing the parent. For each node, return whether its subtree is a BST, the minimum and maximum values in that subtree, and the number of valid BST subtrees within it.
For a null node, return (true, +inf, -inf, 0). For a leaf, return (true, node.val, node.val, 1). For an internal node, combine left and right results: the subtree is a BST if both children are BSTs and left.max < node.val < right.min; then update min/max and add 1 to the count if valid.
Recursively compute the tuple for each node, incrementing a global counter or returning the count. At the end, the count at the root is the total number of valid BST subtrees.
State that the algorithm runs in O(n) time and O(h) space due to recursion. Discuss edge cases: empty tree, single node, all nodes forming a BST, and trees with duplicate values if allowed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Select a problem you solved confidently during the OA, then narrate your solution in a clear, structured way: restate the problem, explain your initial thoughts, describe the algorithm, analyze complexity, and mention any edge cases. Since it's AI-moderated, speak as if explaining to a human interviewer—be concise but thorough, and avoid dead air.
Pro tip: Choose a problem where you can clearly articulate the trade-offs between your initial approach and the optimal solution, as this demonstrates deeper understanding and problem-solving maturity. Also, explicitly state your assumptions and constraints before diving into the solution to show you think before coding.
Briefly summarize the problem in your own words, including input/output format, constraints, and any assumptions. This ensures you and the interviewer (or AI) are aligned.
Explain a naive or brute-force solution first, including its time and space complexity. This shows you can start simple and then optimize.
Describe your improved approach step-by-step, focusing on the key insight or data structure used. Walk through a small example to illustrate.
State the time and space complexity of your final solution, and mention any edge cases you considered (e.g., empty input, large values, duplicates).
Briefly explain how you would test the solution, and mention any further optimizations or alternative approaches if relevant.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
No specific problem name I can share but it was a classic DP setup.
First, restate the problem in your own words and clarify constraints and edge cases. Then, identify the optimal substructure and overlapping subproblems, define the DP state and recurrence, and implement iteratively with careful indexing. Finally, test with small examples and analyze time and space complexity.
Pro tip: In timed OAs, start with a brute-force recursive solution to validate correctness, then optimize using memoization or tabulation. Always write down the DP table dimensions and base cases before coding to avoid off-by-one errors.
Restate the problem, ask clarifying questions about input size, constraints, and expected output. Identify if it's a classic DP pattern (e.g., knapsack, LCS, coin change).
Determine what each DP state represents (e.g., dp[i] = max value up to index i). Write the recurrence relation and base cases clearly.
Decide between top-down memoization (easier to derive) and bottom-up tabulation (often more efficient). Consider space optimization if possible.
Implement the solution with clear variable names and comments. Test with provided examples, edge cases (empty input, large values), and trace through small inputs.
State the time and space complexity of your solution. Discuss potential optimizations or trade-offs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
First, clarify the problem constraints and edge cases, then choose the appropriate traversal algorithm (BFS/DFS for graphs, iterative/recursive for linked lists). Implement a clean solution with optimal time and space complexity, and test with small examples before finalizing.
Pro tip: In timed OAs, prioritize writing a working brute-force solution first, then optimize if time permits; this ensures partial credit and reduces panic.
Restate the problem in your own words and ask clarifying questions about input size, edge cases, and expected output format.
Identify if it's a linked list or graph problem, and select the optimal algorithm (e.g., two pointers, BFS, DFS) based on constraints.
Write clean, modular code with meaningful variable names, handling edge cases like empty lists or cycles.
Walk through your code with a small test case, including edge cases, to verify correctness and catch off-by-one errors.
State the time and space complexity of your solution and discuss potential optimizations if needed.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Read each question carefully, eliminate obviously wrong options, and select the best answer based on fundamental principles. For conceptual questions, recall standard definitions and best practices; for trade-off questions, consider scalability, maintainability, and performance.
Pro tip: Don't overthink; often the simplest, most standard answer is correct. If unsure, choose the option that aligns with widely accepted best practices and avoids over-engineering.
Read the question and all options thoroughly to grasp what is being asked. Identify key terms and concepts.
Rule out options that are clearly incorrect or violate fundamental principles. This narrows down the choices.
Use your knowledge of system design, design patterns, OOP, and networking to evaluate remaining options. Consider trade-offs and context.
Choose the option that best fits the question, balancing correctness and practicality. Avoid overcomplicating.
Quickly double-check your choice for consistency and to catch any misread details.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clearly restating the original problem, your initial solution, and its complexity. Then systematically analyze how the new constraint or requirement impacts the solution, discussing trade-offs and potential optimizations. Conclude by summarizing the revised approach and its complexity, showing adaptability and structured thinking.
Pro tip: Always connect the DSA discussion to real-world engineering trade-offs, such as scalability, maintainability, and cost, to demonstrate maturity beyond textbook solutions.
Briefly describe the problem, your initial approach, and its time/space complexity to establish a baseline.
Ask clarifying questions if needed, then explicitly state how the new constraint changes the problem's scope or assumptions.
Evaluate how the new constraint affects the current algorithm's correctness, efficiency, and edge cases, identifying bottlenecks.
Suggest one or more modified or alternative algorithms, comparing their trade-offs in terms of time, space, and implementation complexity.
Conclude with the recommended approach, its complexity, and how you would test or validate it under the new constraint.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the problem requirements and constraints, then outline a high-level design that separates concerns and follows SOLID principles. Discuss trade-offs between different design choices, and finally implement the core components with clean, extensible code. Emphasize how your design supports future changes and scalability.
Pro tip: Demonstrate design maturity by explicitly stating assumptions and non-functional requirements (e.g., scalability, maintainability) early, and show how your design decisions address them. This signals you think beyond just making it work.
Ask questions to understand functional and non-functional requirements, such as expected load, latency, consistency, and extensibility needs. Confirm any assumptions with the interviewer.
Sketch the main components, their responsibilities, and interactions. Apply separation of concerns and identify key abstractions, interfaces, and data flow.
Compare alternative designs (e.g., monolithic vs. microservices, SQL vs. NoSQL) and justify your choices based on requirements, discussing pros and cons.
Dive into critical components, define classes/interfaces, and implement core logic with clean, readable code. Highlight design patterns used and how they improve flexibility.
Summarize how the design meets requirements, and discuss potential future extensions or improvements, showing awareness of evolving needs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Mixed in with the implementation question.
Start by clarifying the problem scope, functional and non-functional requirements, and constraints before diving into design. Then propose a high-level architecture, drill into critical components, and explicitly discuss trade-offs and alternatives. Finally, address scalability, reliability, and operational concerns, tying back to business goals.
Pro tip: Demonstrate leadership by proactively identifying potential failure points and mitigation strategies, and by asking about team structure and existing tech stack to tailor your design to Majid Al Futtaim's context.
Ask questions to understand the problem's scope, expected scale, latency, consistency, and budget constraints. Confirm functional and non-functional requirements with the interviewer.
Sketch the main components (e.g., clients, load balancers, services, databases, caches) and their interactions. Keep it simple and focus on the core flow.
Choose one or two critical components (e.g., data storage, messaging) and discuss detailed design choices, data models, and algorithms. Explain how they meet the requirements.
Compare your choices with alternatives (e.g., SQL vs NoSQL, monolith vs microservices) and justify decisions based on requirements. Acknowledge pros and cons.
Discuss how the design scales (horizontal vs vertical), handles failures (redundancy, retries, circuit breakers), and monitors performance. Mention operational aspects like deployment and observability.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.