My 2025 Vanguard HackerRank: Questions I Faced and Solved

Seraphina

·November 13, 2025

·7 min read

I passed the Vanguard Hackerrank in 2025 here are my real interview questions

My Vanguard SDE HackerRank assessment had a total of seven questions. Four of them were multiple-choice, and the other three were coding problems. The questions were not very difficult, and the coding problems could be solved using brute-force approaches.

In the following, I will show all the questions I encountered and explain my approach to solving the three coding problems.

I am really grateful for the tool Linkjob.ai, and that's also why I'm sharing my entire OA interview experience here. Having an undetectable AI assistant during the interview is indeed very convenient.

Vanguard Hackerrank Questions 2025

Multiple-Choice Questions

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Coding Question 1: Spam Detection

The logic here is straightforward, focusing on case-insensitive matching and frequency counting. I first clarified the rules: a subject is considered spam if it contains at least two spam words (repeated words count) and matching is case-insensitive.

My approach:

Convert all spam words to lowercase and store them in a set for quick lookup.
For each email subject, split it into words, convert each word to lowercase, and count how many appear in the spam word set.
If the count is 2 or more, mark it as spam; otherwise, mark it as not_spam.

For example, in the sample input, the first subject “free prize worth millions” contains “free” and “millions” as spam words, count = 2, so it is marked spam. In the second subject, “carefree” is not considered a spam word because it is not exactly “free”, so count = 0, marked not_spam.

Coding Question 2: Are they Pangrams

This problem requires checking if a string contains all 26 lowercase letters. A pangram is defined clearly, so the main task is counting unique letters in the string.

My approach:

Extract all letters from the string while ignoring spaces.
Store the letters in a set and check if the set size is 26.
If yes, mark 1; otherwise, mark 0. Concatenate all results at the end.

For example, in the sample, the first sentence “pack my box with five dozen liquor jugs” contains all 26 letters, so it is 1. The second sentence is missing many letters, so it is 0. Result: “10”.

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Coding Question 3: Missing Words

First, I confirmed the definition of a subsequence: words in t appear in s in the same order but not necessarily consecutively. The goal is to find words in s that are not in t, preserving s’s order.

I used a two-pointer approach:

Split s and t into word lists, s_words and t_words.
Initialize two pointers i (for s_words) and j (for t_words), both starting at 0.
Traverse s_words:
- If s_words[i] == t_words[j], the word exists in t, move j forward by 1.
- Otherwise, the word is missing and added to the result list.
- Always move i forward in each loop.
Return the result list at the end.

For example, s = [“I”, “like”, “cheese”], t = [“like”].

i = 0, s[0] != t[0], add “I” to the result.
i = 1, s[1] == t[0], move j to 1 (beyond t’s length).
i = 2, add “cheese” to the result. Final result: [“I”, “cheese”].

Preparation Resources

Study Materials

Insights from Tech Communities and Career Platforms

I frequently checked the Vanguard sections on Glassdoor and Blind to gather insights from previous candidates about OA focus areas. These platforms consistently highlight that foundational coding ability is the core requirement, and that candidates often lose points on rule interpretation and handling edge cases. Another advantage is that they offer preparation guidance helping me avoid irrelevant topics and stay focused on what actually matters. Such as practicing fundamental algorithmic logic and paying attention to efficiency.

General Problem-Solving Methods from Video Platforms

I used YouTube to organize general frameworks for solving coding problems, focusing on the core ideas: how to apply basic data structures, how to break down standard requirements, and how common algorithms work. The visual code walkthroughs helped me quickly build problem-solving intuition and handle different types of OA questions more effectively.

Practice Platforms

HackerRank’s Basic Algorithm Sets

I focused on HackerRank’s Easy-to-Medium foundational algorithm questions, especially those tagged with Counting, Matching, and Sequence. I practiced under OA-like timing—15–20 minutes per question—to get used to the platform’s test case patterns, such as handling duplicates and multi-condition logic. This helped me prepare targeted strategies in advance.

LeetCode High-Frequency Fundamentals

I prioritized LeetCode’s high-frequency Easy-to-Medium fundamentals, concentrating on core skills like basic counting logic, sequence processing, tokenization, and boundary handling. After solving each problem, I compared different solution approaches to learn more efficient patterns, ensuring that my code remained robust and fast enough for OA time limits.

Vanguard HackerRank Assessment Overview

Test Positioning

From an overall design perspective, Vanguard’s HackerRank test is not aimed at tackling highly difficult problems. Its core focus is on assessing fundamental engineering skills. The question difficulty mostly ranges from easy to medium and does not involve complex algorithms. The problems primarily center on string manipulation and similar high-frequency workplace scenarios. These scenarios represent basic modules in everyday enterprise development, covering the essential skills required for almost all technical positions.

The key value of this positioning is the ability to quickly identify candidates who can translate basic business logic into stable code. For companies, advanced algorithm skills can be evaluated later in technical interviews, but solid foundational engineering skills are essential for employees to quickly contribute. For candidates, these tests emphasize rigor and practicality over algorithmic talent. With strong fundamentals and careful problem reading, success is highly achievable.

Skills Assessment

Tool Application

Using basic operations such as string splitting, case conversion, and data structure usage, the test assesses familiarity with programming language APIs. Mastery of these tools forms the basis for efficient coding. For example, efficiently using collections to speed up lookups or leveraging concise APIs for string processing directly affects code efficiency and clarity.

Rule Translation

Converting natural language rules into code logic is a key step in evaluating a candidate’s ability to understand requirements and model problems. This skill determines whether a developer can accurately interpret business needs. Many development errors stem not from technical difficulty but from misinterpreting requirements. The test uses detailed problem statements to identify candidates who are careful and logically precise.

Engineering Mindset

Implicit evaluation of handling edge cases and optimizing efficiency gauges engineering thinking. In professional development, code must not only function correctly but also remain stable, efficient, and maintainable. Test designs such as repeated spam word counting and double-pointer optimizations help identify candidates who consider extreme scenarios and seek optimal solutions. These candidates reduce potential online issues caused by insufficient code robustness after joining.

Job Relevance

It is clear from the test content that Vanguard’s HackerRank is highly aligned with the needs of business-focused technical roles rather than research-oriented positions. This is reflected in three aspects:

Scenario Matching

The selected string manipulation tasks mirror the daily work focus of business technical roles. Passing the test indicates that a candidate possesses the foundational skills to handle such tasks, enabling a smooth transition into workflows and reducing onboarding costs for the company.

Skill Alignment

The abilities assessed (rule translation, handling edge cases, and efficiency optimization) are core competencies for business technical roles. Most development work revolves around implementing business rules, managing exceptions, and ensuring stable interfaces rather than solving complex algorithmic problems. Such tests help companies identify candidates who can deliver quickly and with fewer errors, aligning with the “fast delivery and stable operation” goal of business roles.

Barrier Balance

The easy-to-medium difficulty ensures efficient screening while remaining inclusive. This threshold suits the talent needs of business technical roles: companies seek candidates with solid fundamentals and a meticulous attitude rather than algorithmic geniuses. By controlling test difficulty, companies can accurately identify the target candidate profile.

FAQ

Which programming languages could be used for Vanguard’s HackerRank assessment?

I used C++ at the time. Other languages were also allowed. The choice could be based on personal preference.

When dealing with implicit rules in the problem statement, how can they be identified accurately during the test?

In my experience, the most effective approach is reverse-inferring from the examples and leaving time to validate edge cases. First, study the sample test cases carefully and deduce any unstated constraints from their behavior. After completing the code, reserve 2–3 minutes to test extreme scenarios such as empty inputs or boundary values to ensure that all hidden requirements are fully covered.