Categorize Box According to Criteria - Practice Coding Problems

Categorize Box According to Criteria - Problem

Math Easy

Imagine you work at a shipping company and need to categorize packages for proper handling and shipping costs. Given the dimensions (length, width, height) and mass of a box, you must determine its category based on specific criteria.

A box can be classified as:

"Bulky" if any dimension ≥ 10⁴ units OR volume ≥ 10⁹ cubic units
"Heavy" if mass ≥ 100 units

Based on these properties, return the appropriate category:

"Both" - if the box is both Bulky and Heavy
"Bulky" - if the box is Bulky but not Heavy
"Heavy" - if the box is Heavy but not Bulky
"Neither" - if the box is neither Bulky nor Heavy

Note: Volume = length × width × height

Input & Output

example_1.py — Standard Case

$ Input: length = 1000, width = 35, height = 700, mass = 300

› Output: "Heavy"

💡 Note: The box is not bulky (no dimension >= 10^4 and volume = 24,500,000 < 10^9) but is heavy (mass = 300 >= 100), so it's categorized as "Heavy".

example_2.py — Both Bulky and Heavy

$ Input: length = 200, width = 50, height = 800, mass = 50

› Output: "Neither"

💡 Note: The box is not bulky (no dimension >= 10^4 and volume = 8,000,000 < 10^9) and not heavy (mass = 50 < 100), so it's "Neither".

example_3.py — Large Dimension

$ Input: length = 10000, width = 1, height = 1, mass = 1

› Output: "Bulky"

💡 Note: The box is bulky (length = 10^4 meets the dimension criteria) but not heavy (mass = 1 < 100), so it's "Bulky".

Constraints

1 ≤ length, width, height ≤ 10⁵
1 ≤ mass ≤ 10³
Watch out for integer overflow when calculating volume

Visualization

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Understanding the Visualization

Measure Dimensions

Check if any dimension >= 10,000 units (like checking if box fits standard conveyor)

Calculate Volume

If dimensions OK, calculate total volume to see if >= 1 billion cubic units

Determine Bulky Status

Box is bulky if either dimension or volume criteria met

Check Weight

Weigh the package - heavy if >= 100 units

Assign Category

Combine bulky and heavy flags to determine final handling category

Key Takeaway

🎯 Key Insight: Evaluate bulky and heavy criteria independently using boolean flags, then combine with simple conditional logic for clean, maintainable code.

Asked in

a Amazon 15 ⊞ Microsoft 8 G Google 5 f Meta 3

The optimal approach uses boolean flags to independently evaluate bulky and heavy criteria, then combines them with simple conditional logic. Key insight: check dimension criteria first (cheaper than volume calculation) and use appropriate data types to avoid integer overflow when calculating volume.

Common Approaches

Approach	Time	Space	Notes
✓ Direct Conditional Evaluation	O(1)	O(1)	Check all criteria directly with nested conditions

Direct Conditional Evaluation — Algorithm Steps

Check if any dimension >= 10^4
Calculate volume and check if >= 10^9
Determine if box is bulky (either condition true)
Check if mass >= 100 (heavy condition)
Use nested if-else to determine final category

Visualization

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Step-by-Step Walkthrough

Check Dimension Criteria

Check if any dimension >= 10^4

Check Volume Criteria

Calculate and check if volume >= 10^9

Determine Bulky Status

Set bulky flag based on either condition

Check Mass Criteria

Check if mass >= 100 for heavy status

Nested Classification

Use nested if-else to determine final category

Code -

solution.c — C

#include <stdio.h>
#include <string.h>

char* categorizeBox(int length, int width, int height, int mass, char* result) {
    // Check all bulky conditions
    int isBulky;
    if (length >= 10000 || width >= 10000 || height >= 10000) {
        isBulky = 1;
    } else if ((long long)length * width * height >= 1000000000LL) {
        isBulky = 1;
    } else {
        isBulky = 0;
    }
    
    // Check heavy condition
    int isHeavy = mass >= 100;
    
    // Determine category with nested conditions
    if (isBulky && isHeavy) {
        strcpy(result, "Both");
    } else if (isBulky && !isHeavy) {
        strcpy(result, "Bulky");
    } else if (!isBulky && isHeavy) {
        strcpy(result, "Heavy");
    } else {
        strcpy(result, "Neither");
    }
    return result;
}

Time & Space Complexity

Time Complexity

⏱️

O(1)

Constant time as we perform a fixed number of comparisons regardless of input values

✓ Linear Growth

Space Complexity

O(1)

Only uses a constant amount of extra space for variables

✓ Linear Space

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Input & Output

Constraints

Visualization

Related Problems

Common Approaches

Direct Conditional Evaluation — Algorithm Steps

Visualization

Code -

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