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Quizzes > Quizzes for Business > Technology

Developer Numeric Reasoning Quiz: Test Your Skills

Sharpen Your Numeric Problem-Solving Abilities Today

Difficulty: Moderate
Questions: 20
Learning OutcomesStudy Material
Colorful paper art displaying a developer numeric reasoning quiz theme.

Embark on a dynamic Developer Numeric Reasoning Quiz featuring 15 multiple-choice challenges designed to elevate your calculation and data analysis skills. Perfect for aspiring developers and seasoned coders looking to refine numeric problem-solving in real-world coding scenarios. Every question is fully customizable in our intuitive quizzes editor, so you can tailor the experience to your learning goals. For a deeper dive into reasoning skills, try the Logical Reasoning Quiz or master core concepts with the Software Developer Fundamentals Quiz .

What is the sum of the first five positive integers?
15
25
20
10
The first five positive integers are 1, 2, 3, 4, and 5. Their sum is 1+2+3+4+5 = 15.
If a function iterates through an array of n elements exactly once, what is its time complexity in Big O notation?
O(log n)
O(1)
O(n)
O(n^2)
Visiting each element exactly once results in a linear relationship between operations and n. Thus the complexity is O(n).
A loop doubles i starting from 1 while i ≤ 16. How many iterations occur?
5
6
4
8
The values of i are 1â†'2â†'4â†'8â†'16, then doubling to 32 exceeds 16. That is 5 iterations in total.
Convert 2500 bytes to kilobytes (1 KB = 1024 bytes). Approximately how many KB is that?
2.44 KB
2.50 KB
2.30 KB
2.60 KB
Divide 2500 by 1024 to convert to KB: 2500/1024 ≈ 2.44 KB. This is the approximate value.
Identify the next number in the sequence: 2, 4, 6, 8, ...
10
9
11
12
The sequence increases by 2 each time, so after 8 the next number is 8 + 2 = 10.
An algorithm uses two nested loops, each running from 0 to n-1. What is its time complexity?
O(n^2)
O(n log n)
O(n)
O(log n)
Two nested loops each iterating n times produce n à - n operations, which is O(n^2).
Calculate 15% of 200.
20
15
30
25
15% of 200 is (15/100) Ã - 200 = 30.
A recursive function halves its input size each call until reaching 1. What is its time complexity?
O(n)
O(n log n)
O(1)
O(log n)
Each call reduces the problem size by half, leading to a logarithmic number of calls, O(log n).
In a 3:4 ratio of CPU to GPU usage totaling 700 units, how many units does the CPU use?
250 units
300 units
400 units
350 units
CPU usage is 3/(3+4) of 700, which equals 3/7 Ã - 700 = 300 units.
Evaluate the expression (3 + 5) * (2^3).
64
56
48
40
First compute inside parentheses: 3+5=8. Then 2^3=8, and 8Ã - 8=64.
Performing binary search on 1024 sorted items requires at most how many comparisons?
10
9
12
11
Binary search halves the search space each time; log2(1024) = 10, so a maximum of 10 comparisons.
A loop starts at i=1 and doubles i each iteration until i > 32. How many iterations execute?
4
6
5
7
Values: 1â†'2â†'4â†'8â†'16â†'32, then doubling to 64 exceeds 32. That is 6 iterations.
A 1 GB file must be split into chunks of 64 MB each. How many chunks are produced?
8
16
12
18
1 GB = 1024 MB, and 1024/64 = 16 chunks.
In many programming languages, what is the result of integer division 7/2?
3
4
2
3.5
Integer division discards the remainder, so 7 divided by 2 yields 3.
If an O(n log n) algorithm processes 1000 items vs. 100 items, approximately how many times slower is it?
25
10
15
20
Compute ratio: (1000à - log2(1000))/(100à - log2(100)) ≈ (1000à - 10)/(100à - 7) ≈ 10000/700 ≈ 14.3, round to 15.
A loop runs i from 1 to n and performs an O(i) operation each iteration. What is the overall time complexity?
O(log n)
O(n)
O(n^3)
O(n^2)
Summing operations from i=1 to n gives O(1+2+...+n) = O(n(n+1)/2) = O(n^2).
Using Dijkstra's algorithm with an adjacency list and a min-heap on a graph with V vertices and E edges yields what time complexity?
O(V log V)
O(E^2)
O(E log V)
O(V^2)
With a min-heap, each edge relaxation involves a heap operation, so the complexity is O(E log V).
Given a data point x=60, dataset mean μ=50 and standard deviation σ=5, what is the z-score?
0.2
1.5
10
2
The z-score is (xâˆ'μ)/σ = (60âˆ'50)/5 = 10/5 = 2.
Compute the sum of the geometric series 3 + 6 + 12 + ... + 3·2^4.
93
96
108
99
Terms are 3·2^k for k=0..4; sum =3(2^5âˆ'1)/(2âˆ'1)=3(32âˆ'1)=3à - 31=93.
Floyd - Warshall algorithm runs in O(V^3). For V=50, how many basic operations approximate V^3?
250,000
125,000
100,000
75,000
Compute 50^3 = 50Ã - 50Ã - 50 = 125,000 operations, matching the cubic relationship.
0
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Learning Outcomes

  1. Analyse numerical data patterns for coding tasks
  2. Evaluate algorithmic efficiency in numeric scenarios
  3. Master calculation-based problem-solving techniques
  4. Identify quantitative relationships in software problems
  5. Demonstrate precision in arithmetic operations
  6. Apply number-driven logic to development challenges

Cheat Sheet

  1. Master the Sliding Window Technique - Think of it as scanning your array with a window that slides smoothly. With each move, you subtract the element that leaves and add the new one, avoiding a full re-sum and boosting efficiency. Common Coding Patterns on Medium
  2. Understand the Two Pointers Strategy - Picture two friendly pointers gliding from both ends toward the center to find matches faster than a brute-force search. This duo is perfect for problems like "Two Sum" on a sorted array, saving you precious time. Common Coding Patterns on Medium
  3. Grasp Greedy Algorithms - Like a snack-lover grabbing the biggest chip first, greedy algorithms take the best immediate choice hoping it leads to global glory. They usually run quickly, but watch out for traps where local wins aren't global wins. Common Coding Patterns on Medium
  4. Learn Prefix Sum for Efficient Range Queries - Imagine building a staircase of sums so you can leap to any level instantly. With P[j] - P[i - 1], you get your subarray sum in constant time - pure magic for range queries. 15 Coding Patterns on Medium
  5. Explore Dynamic Programming (DP) Patterns - Break your epic quest into smaller missions, solve each one, and remember the results to avoid repeat work. Familiarize yourself with classics like Fibonacci, Knapsack, and Longest Common Subsequence to level up your problem-solving. 15 Coding Patterns on Medium
  6. Analyze Algorithmic Efficiency with Big O Notation - Big O isn't a fancy soda - it's how we size up runtime and memory usage as inputs grow. Understanding O(log n) versus O(n) helps you pick the fastest path through any data jungle. Wikipedia: Algorithmic Efficiency
  7. Implement Divide and Conquer Strategies - Slice your problem into bite-sized chunks, solve each piece, and merge the results for victory. Merge Sort is a classic example: split, sort, then reunite for a beautifully ordered array. Wikipedia: Algorithmic Efficiency
  8. Practice Recursion and Iteration - Recursion is like Russian dolls - functions calling themselves until the base case pops up - while iteration loops patiently, using less memory but sometimes more code juggling. Choose the style that best fits your problem's constraints and stack limits. Algorithmic Efficiency Examples
  9. Optimize with Memoization - Turn your functions into memory wizards by caching results of expensive calls. This trick transforms naive recursive solutions, like Fibonacci, from exponential time to linear, making you the hero of efficiency. Algorithmic Efficiency Examples
  10. Understand Space Complexity - Plot your algorithm's memory map to avoid runaway usage; sometimes a bit of extra space can save a ton of time. Balancing time and space is the secret handshake of top-tier programmers. Algorithmic Efficiency Examples
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