SDE-2 Interview: Data Structures u0026 System Design Concepts Quiz

Explore key data structures and system design principles relevant to SDE-2 interviews, including managing tag popularity and choosing efficient approaches for real-time systems. Sharpen your foundational understanding with this beginner-friendly quiz covering essential algorithms and practical coding scenarios.

1. Tag Popularity Tracking

   Which data structure is most suitable for efficiently mapping tag names to their current popularity counts in a tag management system?

   1. Hash map (or unordered_map)
   2. Array
   3. Linked list
   4. Queue

   Explanation: A hash map allows quick lookups, insertions, and updates of tag-popularity pairs, making it ideal for managing popularity counts. Arrays lack direct mapping to tag names unless tags are mapped to indices first. Queues and linked lists do not support fast arbitrary value retrieval by key, so they are inefficient for this task.

2. Grouping Tags by Popularity

   If you want to quickly find all tags with a certain popularity value, which data structure should you use to group tags by their popularity?

   1. Sorted array of tags
   2. Priority queue of tags
   3. Map from popularity to a set of tags
   4. Stack

   Explanation: A map from popularity to a set of tags lets you access all tags with a specific count in constant or near-constant time. A sorted array would make insertion and deletion slower. A stack and a priority queue do not naturally group tags by the same popularity, making them unsuitable for this requirement.

3. Updating Maximum Popularity

   After incrementing or decrementing a tag's popularity, which variable is useful to track the tag with the highest current popularity in the system?

   1. minCount
   2. curIndex
   3. maxPop
   4. topTag

   Explanation: maxPop is used to record the highest popularity value among all tags, so you can efficiently identify the current top tag. minCount would track the minimum, not maximum. curIndex is unrelated in this context. topTag could be misleading as popularity may be shared by multiple tags.

4. Decrementing Tag Popularity

   When the popularity of a tag is decremented to zero, what should the system do with that tag in the tracking data structures?

   1. Move the tag to a priority queue
   2. Set the popularity to a negative number
   3. Leave the tag with a count of zero
   4. Remove the tag from all data structures

   Explanation: Tags with zero popularity are typically removed to keep the system efficient and prevent unnecessary memory use. Leaving tags with zero counts can waste space. Negative popularity is not meaningful for this use case, and moving the tag to a priority queue is unnecessary if it's no longer popular.

5. Getting the Most Popular Tag

   To efficiently return the tag with the highest popularity, what is the best approach given the described data structures?

   1. Sort all tags by their popularity every time
   2. Randomly pick a tag
   3. Search through all tags linearly
   4. Check the set of tags corresponding to maxPop

   Explanation: By maintaining a set of tags indexed by their popularity and a separate maxPop variable, you can quickly find the top tag without searching or sorting. Sorting or linear search is much slower, while picking randomly may not yield the most popular tag.

6. Algorithm Complexity

   What is the average time complexity for updating a tag’s popularity using a hash map in this system?

   1. O(log n)
   2. O(n)
   3. O(1)
   4. O(n^2)

   Explanation: A hash map provides average constant-time complexity, O(1), for insertion and updating operations. O(n) and O(n^2) would indicate linear or quadratic time, which is not the case here. O(log n) is typically seen with trees, not hash maps.

7. Handling Ties in Maximum Popularity

   When several tags share the same highest popularity value, what is a reasonable way to select a tag to return from getMostPop()?

   1. Return the tag with the shortest name
   2. Return all tags with popularity zero
   3. Decrease the popularity of all tied tags
   4. Return any one of the tags with maxPop

   Explanation: Returning any tag from the set with maxPop meets the requirements and is efficient. Returning tags with zero popularity is irrelevant to popularity ranking. Decreasing popularity is not required, and choosing by short name is arbitrary and unnecessary unless specified.

8. Design Principle: Consistent Updates

   Why is it important to update both the tagToPop and popToTags data structures during incPop or decPop operations?

   1. To avoid syntax errors
   2. Only tagToPop needs updates
   3. To keep the system’s state accurate and synchronized
   4. To speed up memory allocation

   Explanation: Updating both ensures tag popularity information is correct and that both lookups (by tag and by popularity) remain consistent. Only updating one ignores the relationship between them. Memory allocation speed is not directly relevant, and syntax errors occur for incorrect code, not logic.

9. Real-Time System Consideration

   In the context of a real-time popularity tracking system, why is it important to have all main operations complete quickly?

   1. To display fancy graphics
   2. To use more memory
   3. To ensure tags are always sorted alphabetically
   4. To handle many user actions without delays

   Explanation: Efficient operations are necessary in real-time systems to handle frequent updates and queries from many users with minimal delays. Using more memory or sorting tags alphabetically are not core objectives. Displaying graphics is unrelated to backend system efficiency.

10. Extensible System Design

    If you want to easily add new features to the tag management system in the future, what is a good system design principle to follow from the start?

    1. Avoid documenting your design decisions
    2. Write all code in a single long function
    3. Hardcode all tag names
    4. Use modular and well-separated components

    Explanation: Modular design allows for flexibility and easier future enhancements as each component can be updated independently. Writing everything in one function leads to messy, hard-to-maintain code. Lack of documentation and hardcoding tags both reduce adaptability and scalability.