Fetch, group, and print paginated activities

Quick Overview

This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Fetch, group, and print paginated activities states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Fetch, group, and print paginated activities

Company: Peregrine

Role: Software Engineer

Category: Coding & Algorithms

Difficulty: medium

Interview Round: Technical Screen

##### Question Given a paginated API that returns (totalCount, activities[]) for a requested page, implement code that iterates through all pages and prints every activity. After fetching all activities, group them into clumps where each clump contains activities sharing the same type and name. Implement a Clump class with attributes: activities list, type, and name, and return the clumps. Update the output so each activity shows the user’s name (retrieved from a separate getUserName(userId) API) instead of the user ID.

Quick Answer: This interview question evaluates algorithm design, data structures, correctness, complexity, edge cases, and implementation details in a realistic interview setting. A strong answer for Fetch, group, and print paginated activities states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Solution

# Solution Alignment The prompt asks for an implementation-level answer. The safest way to present it is to define the state, maintain clear invariants, then walk through complexity and tests. ## Problem Restatement ##### Question Given a paginated API that returns (totalCount, activities[]) for a requested page, implement code that iterates through all pages and prints every activity. After fetching all activities, group them into clumps where each clump contains activities sharing the same type and name. Implement a Clump class with attributes: activities list, type, and name, and return the clumps. Update the output so each activity shows the user’s name (retrieved from a separate getUserName(userId) API) instead of the user ID. ## Recommended Approach Start with a brute-force baseline to confirm correctness, then identify the repeated work or ordering property that enables a better data structure such as a hash map, heap, stack, queue, two pointers, prefix sums, BFS/DFS, or dynamic programming. Write the implementation around a small invariant and test that invariant directly. ## Correctness The implementation should maintain an invariant after each loop or operation that directly matches the problem statement. At termination, that invariant implies the returned value has considered every valid candidate exactly once, or has preserved the required data-structure state after every API call. ## Complexity State the baseline complexity and the optimized complexity. For most interview constraints, justify why the optimized approach meets the expected input size. ## Edge Cases and Tests Empty and singleton inputs, duplicates, ties, invalid inputs, boundary values, and tests that exercise the main invariant.
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Aug 4, 2025, 10:55 AM
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Fetch, group, and print paginated activities

Given a paginated API that returns (totalCount, activities[]) for a requested page, implement code that iterates through all pages and prints every activity. After fetching all activities, group them into clumps where each clump contains activities sharing the same type and name. Implement a Clump class with attributes: activities list, type, and name, and return the clumps. Update the output so each activity shows the user’s name (retrieved from a separate getUserName(userId) API) instead of the user ID.

Constraints & Assumptions

  • Preserve the scope, facts, inputs, and requested outputs from the prompt above.
  • If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
  • Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask Guidance

  • Clarify input sizes, value ranges, mutability, return format, and tie-breaking.
  • State the target time and space complexity before coding.
  • Call out edge cases such as empty inputs, duplicates, invalid values, overflow, and boundary sizes.

What a Strong Answer Covers Guidance

  • A clear algorithm with the right data structures and enough pseudocode or code-level detail to implement it.
  • A correctness argument that explains why the algorithm covers all required cases.
  • Time and space complexity, plus at least one alternative approach when relevant.
  • Focused tests for normal cases, edge cases, and failure modes.

Follow-up Questions Guidance

  • How would the approach change if the input were streaming or too large for memory?
  • What invariants would you assert in production code?
  • Which tests would catch off-by-one, duplicate, or tie-breaking bugs?

Submit Your Answer to Earn 20XP

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