VMware HackerRank Questions: 2025 OA Prompts, Solutions, and Prep Notes

VMware HackerRank online assessments are usually less about obscure algorithms and more about translating a clean problem statement into efficient, reliable code under time pressure. The 2025 assessment described here included six multiple-choice questions and three coding questions in a 90-minute window. The coding set focused on custom sorting, preprocessing with prefix sums, and reasoning over permutations.

This guide rewrites the experience for ExtraBrain readers who want a practical preparation path, not a shortcut around assessment rules. Use AI tools only where your interviewer, employer, school, assessment platform, and local rules allow assistance, transcription, screenshots, notes, or review. ExtraBrain can be useful for practice sessions, mock interviews, post-session review, and allowed interview workflows because it is a free, local-first Mac desktop AI interview assistant and meeting copilot with live transcription, screen-aware context, bring-your-own AI providers, local options where installed and compatible, and clear privacy controls.

VMware HackerRank OA at a glance

The assessment format was straightforward. There were three coding tasks and several multiple-choice questions. The total time limit was 90 minutes. The coding editor supported mainstream languages such as Python, Java, and C++. The scoring emphasis was on passing visible samples and hidden test cases while keeping time and space complexity within the constraints.

A good preparation plan should prioritize:

• Arrays and strings.
• Sorting with custom keys.
• Hash maps and sets.
• Prefix sums and precomputation.
• Basic permutation reasoning.
• Edge-case testing before final submission.

Question 1: Cardinality Sorting

The first problem was a custom sorting question. Given an array of integers, sort the numbers by the count of 1 bits in each number’s binary representation. If two numbers have the same bit count, sort them by numeric value in ascending order.

For example, 20 in binary is 10100, so its cardinality is 2. For the sample array [1, 2, 3, 4], the binary forms are 1, 10, 11, and 100. The bit counts are 1, 1, 2, and 1, so the sorted result is [1, 2, 4, 3].

Solution idea

The clean solution is to sort by a tuple. The first tuple element is the number of set bits. The second tuple element is the number itself.

In Python, the approach is compact:

def cardinality_sort(arr):
    return sorted(arr, key=lambda x: (bin(x).count("1"), x))

In languages without a built-in bit-count helper, you can implement a small loop that repeatedly checks the lowest bit and shifts the number right. For large inputs, prefer a built-in popcount function when your language provides one.

What this question tests

This question is mainly about reading a custom ordering rule carefully. It also checks whether you can use a language’s sorting API correctly. The most common mistake is sorting only by bit count and forgetting the numeric tie-breaker. Another common mistake is converting the number to binary repeatedly inside a less efficient custom comparator instead of computing a stable key.

Question 2: Distinct Digit Numbers

The second problem asked for counts of numbers in ranges where every digit in the number is distinct. A number such as 123 is valid because all digits are unique. A number such as 121 is invalid because 1 appears twice.

The important constraint was the query volume. With up to 100000 queries and maximum values around 1000000, checking every number inside every query would be too slow. The intended approach was preprocessing.

Solution idea

First, build a validity array from 1 to the maximum possible value. Each index stores 1 if that number has distinct digits and 0 otherwise. Then build a prefix sum array so that each range query can be answered in constant time.

For a query [left, right], the answer is:

prefix[right] - prefix[left - 1]

A Python-style implementation looks like this:

def has_distinct_digits(x):
    text = str(x)
    return len(text) == len(set(text))


def build_prefix(limit):
    prefix = [0] * (limit + 1)
    for value in range(1, limit + 1):
        prefix[value] = prefix[value - 1] + (1 if has_distinct_digits(value) else 0)
    return prefix


def count_distinct_digit_numbers(prefix, left, right):
    return prefix[right] - prefix[left - 1]

For the range [80, 120], this method gives 27. The exact value matters less than the pattern: preprocess once, answer many queries quickly.

What this question tests

This question tests whether you notice that repeated brute force is too expensive. It also tests comfort with prefix sums, which are common in online assessments because they turn repeated range calculations into simple arithmetic. The risk is not the digit check itself. The risk is missing the query constraint and writing a solution that works for samples but times out on hidden tests.

Question 3: Balanced Numbers

The third problem was a permutation reasoning task. A number k was considered balanced if there existed a contiguous subarray of length k that formed a permutation of the numbers from 1 to k. Given a permutation p, the output was a binary string where the kth character represented whether k was balanced.

For example, suppose p = [4, 1, 3, 2]. The positions are:

When considering numbers from 1 to k, track the minimum and maximum positions seen so far. If max_position - min_position + 1 == k, then those numbers occupy exactly k contiguous slots, so k is balanced.

For p = [4, 1, 3, 2], the output is 1011.

Solution idea

Create a position lookup for every value in the permutation. Then iterate k from 1 to n while updating the minimum and maximum positions among values 1..k. If the span length equals k, append 1; otherwise, append 0.

A Python-style implementation is:

def balanced_numbers(p):
    n = len(p)
    pos = [0] * (n + 1)
    for index, value in enumerate(p, start=1):
        pos[value] = index

    min_pos = n + 1
    max_pos = 0
    result = []

    for k in range(1, n + 1):
        min_pos = min(min_pos, pos[k])
        max_pos = max(max_pos, pos[k])
        result.append("1" if max_pos - min_pos + 1 == k else "0")

    return "".join(result)

What this question tests

This question tests whether you can transform a subarray condition into a position-span condition. A brute-force approach would inspect many subarrays and would not scale well. The efficient approach is linear because each value from 1 to n is processed once. The key insight is that a set of k unique values forms a contiguous block if the distance between its minimum and maximum positions is exactly k - 1.

VMware HackerRank interview experience

The VMware HackerRank OA was an independent online assessment rather than a live interviewer-led coding round. The platform provided the usual editor, compile, run, and sample-test workflow. The assessment tracked submissions and evaluated solutions against hidden tests.

The difficulty felt manageable for someone who has practiced common online-assessment patterns. The questions were not unusually theoretical. They rewarded careful reading, clean implementation, and fast recognition of standard techniques.

Common coding themes

The questions aligned with several common assessment categories:

• Array and string operations: Traversal, filtering, sorting, digit manipulation, and edge-case handling.
• Custom sorting: Sorting by multiple keys and applying exact tie-break rules.
• Prefix sums: Precomputing range counts so repeated queries become constant-time lookups.
• Hash maps and lookup arrays: Storing positions, frequencies, or validity flags for fast access.
• Simulation and logic: Turning textual rules into code without overcomplicating the implementation.
• Complexity optimization: Replacing repeated scans with O(n), O(n log n), or O(1) query-time techniques.

Expected difficulty

The overall level was easy to medium. The first question was mostly implementation fluency. The second question required recognizing a preprocessing pattern. The third question required a more subtle observation about permutation positions.

None of the questions required advanced graph algorithms or unusually deep dynamic programming. That does not mean the assessment was trivial. Under time pressure, small errors in indexing, tie-breakers, and range boundaries can still cause failed hidden tests.

Preparation tips for VMware HackerRank

Manage your 90 minutes deliberately

Start by skimming all coding problems before writing code. Identify the easiest one and solve it first. A fast accepted solution on one problem reduces pressure and gives you more room for the trickier prompts.

A practical time split is:

• Spend 5 minutes reading all questions and constraints.
• Spend 20 to 25 minutes on the easiest implementation task.
• Spend 25 to 30 minutes on the preprocessing or data-structure task.
• Spend 25 to 30 minutes on the most reasoning-heavy task.
• Reserve the final 5 to 10 minutes for edge cases and hidden-test thinking.

If you get stuck, write a correct baseline for partial progress, then move on. Return only after you have attempted the other problems. This is especially important when one small bug can consume half the assessment.

Practice the exact patterns

You do not need to spend most of your prep time on very hard problems. For this VMware-style assessment, focus on patterns that appear often in practical online assessments.

Practice these until they feel automatic:

• Counting set bits in your chosen language.
• Sorting by a compound key.
• Building prefix sums from a validity array.
• Answering inclusive range queries with prefix[right] - prefix[left - 1].
• Mapping values to positions in a permutation.
• Testing boundary cases such as one-element arrays, repeated digits, minimum values, and maximum values.

After each practice problem, write down the pattern in one sentence. For example, write “many range queries means build a prefix array” or “permutation subarray can often be checked with min and max positions.” Those short notes are more useful than copying long solutions.

Use ExtraBrain for allowed prep and review

ExtraBrain is useful before and after assessments when used responsibly. During practice, you can use it as an AI interview copilot to explain your solution aloud, capture transcript notes, and identify weak spots in your reasoning. After a mock session, you can review the transcript and screen context to see where you hesitated or missed constraints.

ExtraBrain is available for macOS today, including Apple Silicon and Intel Macs. It supports local-first workflows, local Parakeet transcription, local Gemma 4 on-device AI where installed and compatible, and bring-your-own providers such as Anthropic, OpenAI, custom OpenAI-compatible endpoints, Claude Subscription, and Codex Subscription. A fully local posture requires local Parakeet transcription plus local Gemma 4 where installed and compatible. If you configure external providers, selected prompts, transcript text, screenshots, audio, or context may be sent to those providers depending on your settings.

Use ExtraBrain only in ways that comply with your assessment rules. For a proctored or restricted HackerRank test, that may mean using it only for preparation, mock interviews, notes review, or post-assessment reflection. Do not use any tool to bypass monitoring, impersonate your work, or violate platform instructions.

Recommended practice platforms

HackerRank is useful because the editor and assessment style are close to many real online assessments. Practice the interview preparation kit sections for arrays, strings, sorting, and dictionaries.

LeetCode is helpful for building pattern recognition. Focus on easy and medium problems involving arrays, strings, prefix sums, hash maps, sorting, and permutations. Do not let very hard problems crowd out the basics unless your target role clearly requires them.

GeeksforGeeks is useful as a quick reference when you need a refresher on a known technique. It can help you quickly review prefix sums, bit manipulation, or custom sorting syntax before practice.

The best preparation system is not just solving more problems. It is tracking mistakes by category. Common categories include off-by-one errors, missed hidden cases, unnecessary brute force, and incorrect tie-breakers. Review that mistake list before your assessment.

FAQ

Can I switch programming languages during the VMware HackerRank test?

The platform may support multiple languages, but switching languages during the test is usually a bad use of time. Choose the language in which you can implement arrays, strings, hash maps, sorting, and prefix sums fastest. For most candidates, that means using one language for the whole assessment.

Are all VMware HackerRank test cases visible?

No. Typically, only sample cases are visible. Your final score depends heavily on hidden tests, which is why edge-case checking matters. Before submitting, test small values, maximum-like values, ties, empty or minimal structures when allowed, and boundary ranges.

What should I do if my solution passes samples but fails hidden tests?

First, re-read the exact ordering, indexing, and inclusivity rules. Then test the smallest cases manually. For cardinality sorting, check tie-breakers. For distinct digit ranges, check left = 1 and boundaries around repeated digits such as 99, 100, and 101. For balanced numbers, check whether your positions are one-indexed or zero-indexed consistently.

Is ExtraBrain a responsible way to prepare for VMware HackerRank?

Yes, when used within the rules that apply to your situation. ExtraBrain can help you practice aloud, structure explanations, review transcripts, and build confidence for coding interviews. It should not be used to break assessment rules or conceal prohibited assistance.

See also

• NVIDIA HackerRank assessment prep
• Citadel HackerRank questions and prep notes
• IBM HackerRank test experience
• MathWorks HackerRank assessment guide
• Goldman Sachs HackerRank test notes
• ExtraBrain responsible use