Interview Coder - AI Assistant for Technical Interviews

Facing a timed coding test on HackerRank can feel like racing the clock while untangling tricky algorithm questions under pressure. AI-powered undetectable coding assistance is changing how candidates learn, focusing practice on coding challenges, algorithm questions, data structures, time complexity, test cases, and common problem-solving patterns. This article breaks down common HackerRank interview Questions, explains repeat problem types across companies, and gives clear steps to analyze patterns and sharpen your coding approach. To confidently ace coding interviews by mastering the exact kinds of questions top companies use on HackerRank, many candidates also search for tips like How To Cheat On Codesignal as part of their preparation journey.

To help with that, Interview Coder offers an undetectable coding assistant for interviews that simulates real HackerRank problems, gives step-by-step feedback, and enables you to rehearse mock interviews and improve on weak spots. Hence, you arrive ready for the real test.

Is HackerRank Used for Interviews?

Role of HackerRank in modern hiring processes, what it does for recruiters and candidatesHackerRank functions as an online technical assessment platform that screens coding skills at scale. Recruiters use it to filter large applicant pools, verify programming ability, and rank candidates by performance on objective coding challenges.

Candidates use the same environment to practice HackerRank Interview Questions, simulate coding interviews, and earn skill badges that recruiters can view. The platform shortens the time to hire by letting teams automate initial screening and focus live interviews on design work and culture fit.

Role of HackerRank in Modern Hiring Processes

Companies add HackerRank at several points in their hiring funnel. Common patterns include:

Pre-screen assessments are sent after the application to verify basic coding and algorithm skills.
Automated timed tests that run on a deadline with predefined test cases and scoring
Take-home coding tasks that require a fuller solution and allow the use of external tools.
Live pair programming sessions using CodePair for remote whiteboard and code collaboration.

HR and hiring managers embed platform links in applicant tracking systems so results feed candidate records and trigger next steps.

Types of Skills HackerRank Evaluates

HackerRank measures coding accuracy and also tests problem-solving and algorithmic thinking. Typical skill areas are:

Data structures such as arrays, trees, graphs, and hash tables.
Algorithms like sorting, searching, dynamic programming, greedy methods, backtracking, and divide and conquer.
Language-specific syntax and library use across many languages.
SQL query writing and database logic.
Mathematics and logic puzzles that test analytical thinking.
Domain tasks in machine learning and artificial intelligence are applicable.

Scoring checks correctness on testcases, runtime and memory performance, and code robustness under edge cases.

Stages in Which HackerRank Is Typically Used

Recruiters use HackerRank at early and intermediate stages. Typical stages:

Initial screen after resume review to qualify technical competency.
Secondary screen before scheduling phone interviews to reduce live interview volume.
Take-home or extended exercise for senior roles to assess design and coding depth.
Virtual on-site segment where candidates complete a coding round under time pressure.
Internal practice and training for engineers preparing for internal mobility or promotion.

Examples of Industries and Companies That Rely on HackerRank

Tech firms of all sizes use HackerRank, from large cloud and consumer companies to startups. Companies known for using HackerRank Interview Questions include Google, Amazon, Microsoft, Meta, and Stripe for screening and practice. Finance and consulting firms such as Goldman Sachs and Deloitte use it to test numerical and algorithmic skills.

Healthcare tech, gaming studios, and enterprise software companies use the platform for both engineering and data roles. Startups use HackerRank to validate candidates without a significant investment in interview bandwidth quickly.

Advantages of HackerRank

HackerRank brings objective evaluation that reduces recruiter bias and speeds up hiring through automation. Advantages include:

Standardized scoring and automatic grading of coding submissions.
An extensive problem library for varied challenge types and difficulty.
Timed tests and mock interview modes to simulate real interview pressure.
Company-specific problem sets to mirror target employer expectations.
Integrated code editor and multi-language support, allowing candidates to code in their preferred language.
Analytics and reports that let hiring teams compare candidates quantitatively.

Potential Drawbacks and Limitations

Automated coding tests focus on algorithmic problems and small coding tasks; they do not always reflect system design, teamwork, or long-term engineering judgment. Drawbacks include:

Limited scope for creativity and real-world product context.
Overemphasis on quick problem-solving rather than design thinking.
Candidates can prepare specifically for HackerRank Interview Questions, which may narrow the skill signals.
Some advanced or company-specific questions sit behind paid features or custom assessments.
Anti-cheat measures help, but remote assessments cannot fully replace in-person evaluation of collaboration.

Why HackerRank Is Beneficial for Coding Interview Preparation

HackerRank offers a vast problem library covering essential interview subjects. That includes data structures like linked lists, stacks, queues, trees, and graphs, and algorithm topics such as dynamic programming, greedy methods, backtracking, and search techniques.

The platform organizes content into domain tracks such as mathematics, SQL, functional programming, and machine learning, which helps build depth in focused areas. Practicing HackerRank Interview Questions exposes candidates to a wide variety of problem types that commonly appear in technical interviews.

Structured Learning Paths on HackerRank

HackerRank provides curated learning paths for different skill levels and roles. Examples include:

An Interview Preparation Kit that groups questions similar to those seen in interviews.
Data structures and algorithms tracks that cover fundamentals and advanced topics.

These structured sequences reduce guesswork about what to study and build competence step by step.

Realistic Interview Simulation Features

The platform offers realistic simulation tools such as timed challenges and mock interviews that mirror interview conditions. Contests and timed events let candidates test speed and correctness against a global user base.

Live coding sessions using CodePair let interviewers pair with candidates in real time. Practicing under these conditions improves time management and reduces nerves when facing real technical interviews.

Detailed Solutions and Explanations

HackerRank supplies solutions and explanations that show multiple ways to solve the same question. That helps candidates:

See alternative approaches and algorithm trade-offs.
Learn about code optimization for performance and memory.
Pick up coding style and best practices that matter in production work.

Company Specific Preparation

HackerRank tags many problems by company so that you can work on questions similar to those asked by Google, Amazon, Microsoft, Meta, and others. That makes it easier to practice role and company-specific patterns and to prepare for the kinds of HackerRank Interview Questions you may face.

Certifications and Skill Assessments

HackerRank offers certifications and skill tests that candidates can add to their resumes or LinkedIn profiles. Those badges demonstrate proficiency in languages and technical areas and can help candidates stand out in initial screening.

Community and Support

The platform includes forums and discussion areas where users share solutions and strategies. Peer feedback and community discussion help you debug approaches and discover new techniques when solving HackerRank Interview Questions.

Accessibility and User Experience

HackerRank runs in the browser with an integrated code editor and test harness, so you do not need a separate IDE. It supports many languages, provides progress tracking, and runs code against multiple test cases with runtime and memory feedback.

Considerations When Using HackerRank

Use multiple resources to broaden exposure because each platform emphasizes slightly different problem styles. Some explanations on HackerRank can be brief, so supplement with resources like GeeksforGeeks or algorithm textbooks when you need deeper theory.

Evaluate whether paid features that unlock company-specific questions are worth the cost for your goals. Check for plagiarism detection and anti-cheat rules if you plan to submit assessments under time pressure.

Tips for Using HackerRank Effectively

Start with fundamentals and build a strong base in core data structures and algorithms. Practice consistently and increase problem difficulty in stages. After solving a problem, compare your solution with optimal answers to identify areas for improvement.

Simulate interview conditions with timed tests and mock interviews to hone speed and clarity. Engage in contests to test performance under pressure. Track your progress with the platform analytics and focus on weak areas.

Familiar Candidate Mistakes on HackerRank Assessments

Rushing to pass visible test cases can leave your solution brittle when hidden test cases run. Not handling edge cases and failing to check constraints often causes runtime errors or timeouts. Overreliance on a single platform narrows exposure to other problem formats. Failing to explain trade-offs in follow-up interviews weakens otherwise correct solutions.

How Hiring Teams Use Report Data From HackerRank

Recruiters review pass rates, execution time, and memory usage, along with code quality and test coverage. They combine those metrics with resume data and interview notes to make decisions. Rankings from HackerRank Interview Questions give hiring managers an evidence-based way to prioritize candidate interviews.

Anti-Cheat and Integrity Features

HackerRank uses plagiarism detection and proctoring options to deter cheating. Code similarity checks, time stamps, and optional video proctoring help recruiters maintain trust in remote coding tests. These measures reduce but do not eliminate the risk of dishonest answers.

Preparing for Company-Specific HackerRank Interview Questions

If you target a specific employer, practice problems tagged to that company, and study past interview patterns. Work on system design and take-home projects when aiming for senior roles where depth matters more than pure algorithm speed.

Live Interview Use With CodePair

During live Pair coding sessions, interviewers assess communication, thought process, and the ability to convert ideas into working code. They look for clear problem decomposition, test-driven thinking, and iterative improvement. Practicing clear commentary while coding helps you perform better in these sessions.

Balancing Automated Tests With Human Interviews

Automated assessments handle scale and comparison. Human interviews evaluate collaboration, design, and cultural fit. Companies that combine both reduce false negatives and get a fuller picture of candidate potential.

Costs and Access

HackerRank provides a large free problem set and practice tracks. Advanced company-specific questions and enterprise features require paid subscriptions. Candidates should weigh the value of premium access against time to practice and other free resources.

How to Measure Improvement Using HackerRank

Track solved problem count, success rate on timed tests, reduction in average submission attempts, and movement up leaderboards. Also, monitor how often your solutions pass hidden testcases on first submission.

Questions to Ask Before Taking a HackerRank Test

What language will be allowed?
What is the time limit?
How many questions will be graded?
What are the memory and runtime constraints?
Will there be system design or take-home components?

Pick the Interview Preparation Kit and solve three problems: one easy, one medium, and one hard. Time the medium question to 45 minutes and run it through hidden testcases. Review the optimal solutions and add three new patterns to a personal cheat sheet that you review weekly.

Top 25 HackerRank Interview Questions and Answers

1. Maximum Passengers: Maximize Pickups on a Blocked Grid Round Trip

Problem

A taxi starts at (0,0) and must go to (n-1,n-1) and return. The grid cells are:

>=0: passable
1: passenger (pick once)
-1: blocked

Movement to the station: right or down. Return: left or up. Collect the maximum passengers overall. If there is no valid path, return 0 or -1 per spec.

Approach

Treat the forward and return trip as two walkers moving simultaneously from (0,0) to (n-1,n-1) in k steps. At step k, both positions satisfy x+y=k. Use DP on (k, r1, r2) where r1 and r2 are the row indices for the two walkers. This collapses four coordinates into three. Avoid double-counting when both walkers occupy the same cell.

Solution (Python)

```python
from functools import lru_cache

def max_passengers(grid):
n = len(grid)
if n == 0:
return 0

@lru_cache(None)
def dp(k, r1, r2):
c1 = k - r1
c2 = k - r2
# bounds and obstacles
if not (0 <= r1 < n and 0 <= c1 < n and 0 <= r2 < n and 0 <= c2 < n):
return float('-inf')
if grid[r1][c1] == -1 or grid[r2][c2] == -1:
return float('-inf')

val = 0
if r1 == r2 and c1 == c2:
val = 1 if grid[r1][c1] == 1 else 0
else:
if grid[r1][c1] == 1: val += 1
if grid[r2][c2] == 1: val += 1

if k == 2*(n-1):
return val

best = float('-inf')
# four movement combinations: down/down, down/right, right/down, right/right
for dr1 in (0,1):
for dr2 in (0,1):
nr1 = r1 + dr1
nr2 = r2 + dr2
res = dp(k+1, nr1, nr2)
if res > best:
best = res
return val + best

ans = dp(0, 0, 0)
return max(ans, 0) if ans != float('-inf') else 0
```

Explanation and Complexity

This DP runs over O(n * n * n) states because k ranges from 2n and rows r1, r2 each to n. Each state checks up to 4 transitions. The method avoids simulating forward and backward separately and prevents double-counting when both walkers meet. Use memoization for speed. This is the classic two-walker reduction of the cherry-pickup pattern in interview problems.

2. Minimum Street Lights: Cover a 1-D Road With Minimum Lights

Problem

Given n positions and locations[i] coverage radius for lamp at position i (1-indexed), each lamp covers the closed interval [max(1,i-locations[i]), min(n,i+locations[i])]. All lamps are off initially. Find the minimum number of lamps to switch on to fully cover [1,n].

Approach

Convert each lamp to an interval [L,R]. Sort intervals by L, then greedily pick the lamp with the furthest R that starts at or before the current uncovered position. This is an interval covering greedy.

Solution (Python)

```python
def min_lamps(locations):
n = len(locations)
intervals = []
for i, r in enumerate(locations, start=1):
L = max(1, i - r)
R = min(n, i + r)
intervals.append((L, R))
intervals.sort()

ans = 0
covered = 1
i = 0
while covered <= n:
furthest = -1
while i < n and intervals[i][0] <= covered:
furthest = max(furthest, intervals[i][1])
i += 1
if furthest < covered:
return -1 # impossible to cover
ans += 1
covered = furthest + 1
return ans
```

Explanation and Complexity

This is O(n log n) for sorting, then O(n) scan. The pattern is the standard interval covering used in greedy interview tasks. Use it when intervals are precomputed from radii.

3. Maximize Earnings: Weighted Interval Scheduling for Jobs

Problem

Given n jobs with start, end, and profit, Anirudh picks a set of non-overlapping jobs to maximize his earnings. Return the number of employees and total earnings left for the other employees after he picks optimally (i.e., remaining count and remaining sum).

Approach

Sort jobs by end time. Use dynamic programming where dp[i] = max profit using the first i jobs. For each job i, binary search the last job that ends <= start_i and combine. Track the selected job count as well to compute how many Anirudh picks. Remaining jobs = n - chosen_count. Remaining earnings = total_profit - dp_last.

Solution (Python)

```python
import bisect

def parse_time(s):
# "HHMM" -> minutes
return int(s[:2]) * 60 + int(s[2:])

def maximize_earnings(jobs):
# jobs: list of (start_str, end_str, profit)
arr = []
total = 0
for st, ed, p in jobs:
s = parse_time(st)
e = parse_time(ed)
arr.append((s, e, p))
total += p
arr.sort(key=lambda x: x[1]) # by end
ends = [x[1] for x in arr]
n = len(arr)
dp = [0] * (n+1)
cnt = [0] * (n+1)

for i in range(1, n+1):
s, e, p = arr[i-1]
j = bisect.bisect_right(ends, s) # jobs with end <= s
incl = dp[j] + p
incl_cnt = cnt[j] + 1
if incl > dp[i-1]:
dp[i] = incl
cnt[i] = incl_cnt
else:
dp[i] = dp[i-1]
cnt[i] = cnt[i-1]

chosen = cnt[n]
remaining_jobs = n - chosen
remaining_earnings = total - dp[n]
return remaining_jobs, remaining_earnings
```

Explanation and complexity

Weighted interval scheduling with binary search is O(n log n). Tracking counts alongside profits gives the number of chosen jobs without reconstructing the solution. Use this whenever jobs have profits and you must pick the optimal non-overlapping subset.

4. Network Stream: Largest Repackaged Power-of-Two Chunk

Problem

A stream of packets arrives. For each arrival, add leftover from previous packet, then pack exactly one largest 2^k chunk (largest power of two <= current sum). Remainder carries forward. Report the largest 2^k used across the whole stream.

Approach

For each packet, add carry; find p = highest_power_of_two <= total; record max(p); carry = total - p; move to next packet. Use bit operations to get the highest power fast.

Solution (Python)

```python
def largest_repackaged_packet(packets):
carry = 0
ans = 0
for x in packets:
total = carry + x
if total <= 0:
carry = 0
continue
p = 1 << (total.bit_length() - 1)
ans = max(ans, p)
carry = total - p
return ans
```

Explanation and Complexity

Each packet processes a single largest chunk. bit_length gives the highest power-of-two faster than loops. Complexity O(n). This problem checks bit math and greedy chunk extraction per arrival.

5. Astronomy Lecture: Print Trapezium Star-Dot Pattern

Problem

Given N, print a symmetric trapezium pattern made of '*' and '.' per the sample.

Approach

Pattern splits into top N rows and bottom N rows that are mirror images; produce each row by composing left and right halves.

Solution (Python)

```python
def trapezium(n):
# top half including middle row
for i in range(n):
left = ''.join('*' if j < n-i-1 else '.' for j in range(n))
right = ''.join('.' if j < i else '*' for j in range(n-1))
print(left + right)
# bottom half excluding middle
for i in range(2, n+1):
left = ''.join('*' if j < i-1 else '.' for j in range(n))
right = ''.join('.' if j < n-i else '*' for j in range(n-1))
print(left + right)
```

Explanation and Complexity

String building uses deterministic indices. Complexity O(n^2) characters printed. Use when the interview asks for ASCII pattern generation and index math.

6. Disk Space Analysis: maximum of sliding-window minimums

Problem

Given an array and a window length k, for each contiguous subarray of length k, compute the minimum, then return the maximum among those minima.

Approach

Use a monotonic deque to compute sliding window minima in O(n). Track the max of minima.

Solution (Python)

```python
from collections import deque

def max_of_window_mins(arr, k):
n = len(arr)
dq = deque() # stores indices, increasing arr values
res = float('-inf')
for i in range(n):
# remove out-of-window indices
while dq and dq[0] <= i - k:
dq.popleft()
# keep deque increasing by value
while dq and arr[dq[-1]] >= arr[i]:
dq.pop()
dq.append(i)
if i >= k-1:
res = max(res, arr[dq[0]])
return res
```

Explanation and Complexity

Monotonic deque yields each window's minimum in amortized O(1) time, overall O(n). This is a common sliding-window trick in interviews.

7. Guess the Word: First Maximum Odd-Length Word

Problem

From a list of words, pick the earliest word that has an odd length and that length is the maximum odd length among the list. If no odd-length words exist, print the failure message.

Approach

Scan once to find the maximum odd length, then scan again to see the first word with that length. Single pass can track first occurrence of the current best.

Solution (Python)

```python
def pick_word(words):
best_len = -1
best_word = None
for w in words:
ln = len(w)
if ln % 2 == 1:
if ln > best_len:
best_len = ln
best_word = w
if best_len == -1:
print("Better luck next time")
else:
print(best_word)
```

Explanation and Complexity

Single O(n) pass tracking, max odd length is sufficient. Use this pattern when you must prefer the earliest among equally scoring items.

8. Minimum Start Value: Keep Running Total ≥ 1

Problem

Given a sequence of coin changes (positive or negative), find the minimum starting x so running sum x + prefix never drops below 1.

Approach

Compute prefix sums and track the minimum prefix. Required x = max(1 - min_prefix, 0). Return that minimal x.

Solution (Python)

```python
def min_start_value(nums):
min_pref = 0
cur = 0
for v in nums:
cur += v
if cur < min_pref:
min_pref = cur
return max(1 - min_pref, 0)
```

Explanation and Complexity

Prefix-min trick yields O(n) time and O(1) space. This matches standard problems about maintaining a nonnegative or positive balance.

9. Complex Math: Chain Additions of Complex Numbers

Problem

Given integers a, b, c, treat a+bi as a complex number. Print three lines:1) a + bi2) (a + c) + bi3) (previous sum) + (a + bi)

Approach

Translate straightforward arithmetic and print formatted strings matching spacing.

Solution (Python)

```python
def complex_chain(a, b, c):
def fmt(x, y):
return f"{x} + {y}i"
first = (a, b)
second = (a + c, b)
third = (first[0] + second[0], first[1] + second[1])
print(fmt(*first))
print(fmt(*second))
print(fmt(*third))
```

Explanation and Complexity

This is simple arithmetic and string formatting. Use in object-oriented tasks where operator overloading is explained.

10. Minimum Occurrence: Least Frequent Character With Earliest Tie-Break

Problem

Given a string (ascii range, case matters), find the character with the minimum frequency. If tied, choose the one that appears earliest in the string.

Approach

Count frequencies, then scan the string left to right and pick the first character whose frequency equals the global minimum.

Solution (Python)

```python
from collections import Counter

def least_frequent_char(s):
freq = Counter(s)
minf = min(freq.values())
for ch in s:
if freq[ch] == minf:
return ch
```

Explanation and Complexity

Counting is O(n). The tie-break scan preserves the earliest index. This pattern is useful when frequency plus order matter.

11. Devil Groups: Maximum Group of People Left of Devils

Problem

String contains 'P' for people and markers '@' or '$' for devils. People form groups to the left of a devil; the last trailing people (if any) form a group without a marker. Return the maximum group size; groups that end with a devil count the marker as part of the group per sample behavior.

Approach

Scan characters; count consecutive P's until a marker appears. When marker seen, group size = countP + 1 (marker included). For trailing P's after the last marker, group size = countP. Track max.

Solution (Python)

```python
def max_devil_group(s):
maxg = 0
count = 0
for ch in s:
if ch == 'P':
count += 1
elif ch in ('@', '$'):
grp = count + 1
if grp > maxg: maxg = grp
count = 0
else:
count = 0
# trailing group if any
if count > maxg: maxg = count
return maxg
```

Explanation and Complexity

Simple linear scan O(n). The inclusion of the marker in the counted group matches the provided example and clarifies how to interpret group boundaries in string parsing tasks.

12. Vampire Battle: Minimal Feeds to Outpower the Opponent

Problem

Given n one-digit blood powers as a string, Stephan arrives first and can drink some of the highest-power bloods to reach a total strictly greater than Damon, who will drink the rest. Minimize the number of people Stephan kills and output Stephan’s resulting power.

Approach

Sort blood powers descending. Accumulate from the largest until the prefix sum exceeds the sum of remaining values. Output prefix sum.

Solution (Python)

```python
def stephan_power(s):
arr = sorted([int(ch) for ch in s], reverse=True)
total = sum(arr)
prefix = 0
for i, v in enumerate(arr):
prefix += v
if prefix > total - prefix:
return prefix
return prefix
```

Explanation and Complexity

Sorting takes O(n log n) and accumulation takes O(n). This greedy pattern is typical when minimizing number of items to exceed half the total.

13. Copycat in Exam: Detect Scrambled (Anagram) Copy Ignoring Case

Problem

Check if two words are anagrams, ignoring case. Return 1 if copied, otherwise 0.

Approach

Lowercase both, sort or use character counts, then compare.

Solution (Python)

```python
def is_copied(s1, s2):
a = s1.lower()
b = s2.lower()
return 1 if sorted(a) == sorted(b) else 0
```

Explanation and Complexity

Sorting gives O(n log n) per string; counting yields O(n). Use counting when strings can be long and performance matters.

14. Mr. Robot’s Password: Validate Password Rules

Problem

A password must:

Contain at least one digit
At least one lowercase
At least one uppercase
Contain no space or '/'
Be at least 6 characters

Print "password valid" or "Invalid password, try again".

Approach

Validate with character classification checks and length rules.

Solution (Python)

```python
def validate_password(pw):
if len(pw) < 6 or ' ' in pw or '/' in pw:
print("Invalid password, try again")
return
has_digit = any(ch.isdigit() for ch in pw)
has_lower = any(ch.islower() for ch in pw)
has_upper = any(ch.isupper() for ch in pw)
if has_digit and has_lower and has_upper:
print("password valid")
else:
print("Invalid password, try again")
```

Explanation and Complexity

Straightforward character scanning O(n). This mirrors typical password validation checks asked in coding screens.

15. Weird Terminal: Two Words per Line Count

Problem

Susan can write only up to two words per line. Given a paragraph, compute how many lines are required.

Approach

Split into words by whitespace, count tokens, and answer is ceil(count/2).

Solution (Python)

```python
def terminal_lines(text):
words = [w for w in text.split() if w]
if not words:
return 0
return (len(words) + 1) // 2
```

Explanation and Complexity

Tokenization and integer arithmetic give O(n) time. This is a small string processing task often used to test basic IO and counting.

16. Set Bit Calculator: Total Number of Set Bits Over Numbers

Problem

Given n integers, return the total number of set bits across them.

Approach

Use built-in popcount where available or bin count.

Solution (Python)

```python
def total_set_bits(nums):
total = 0
for x in nums:
total += bin(x).count('1')
return total
```

Explanation and Complexity

Each integer’s bit count is O(bits) and overall O(n * wordsize). Modern interpreters use x.bit_count() (Python 3.8+) for speed.

17. Duplicates: Count Emails to Delete

Problem

Given n roll numbers as received in order, the assistant keeps unique emails and deletes duplicates. Return how many emails are deleted.

Approach

Number of deletions = total entries - number of unique entries.

Solution (Python)

```python
def deletions_needed(arr):
return len(arr) - len(set(arr))
```

Explanation and Complexity

Set-based deduplication is O(n) in expected time. This appears often when deduplication and counting deletions are required.

18. Device Name System: Assign Unique Folder Names

Problem

Given a sequence of requested folder names, return assigned unique names by appending the smallest integer suffix starting at 1 for duplicates.

Approach

Maintain a map mapping base name to the next available integer. For each new request:

If unseen, output base and set next=1
If seen, iterate to find next unused suffix, assign name+suffix, update the map for both base and assigned name

Solution (Python)

```python
def assign_names(requests):
next_idx = {}
result = []
for name in requests:
if name not in next_idx:
result.append(name)
next_idx[name] = 1
else:
k = next_idx[name]
while True:
candidate = f"{name}{k}"
if candidate not in next_idx:
result.append(candidate)
next_idx[name] = k + 1
next_idx[candidate] = 1
break
k += 1
return result
```

Explanation and Complexity

This method keeps searches short by updating the next available suffix. Worst-case repeated duplicates may require extra checks, but for typical inputs, it’s efficient. Use hashing for O(1) expected lookups.

19. Formatting Large Products: Write Product as D × 10^E

Problem

Compute product C = A * (A+1) * ... * B, then represent C = D * 10^E where the last digit of D is non-zero. Return D and E.

Approach

Count factor twos and fives in the product to extract E=min(cnt2, cnt5). For each factor in range, remove all 2s and 5s before multiplying into D so D avoids trailing zeros. Multiply using Python big integers. After the loop, balance residual twos or fives into D, then print D and E.

Solution (Python)

```python
def format_products(A, B):
cnt2 = cnt5 = 0
D = 1
for x in range(A, B+1):
y = x
while y % 2 == 0:
y //= 2
cnt2 += 1
while y % 5 == 0:
y //= 5
cnt5 += 1
D *= y
E = min(cnt2, cnt5)
rem2 = cnt2 - E
rem5 = cnt5 - E
if rem2:
D *= 2 ** rem2
if rem5:
D *= 5 ** rem5
return f"{D} * 10^{E}"
```

Explanation and complexity

This produces exact D and E using integer factor removal. Complexity is O(B-A+1) multiplied by factor stripping. For large ranges, the product grows big, but Python big ints handle growth; in constrained interviews, explain alternatives like using logarithms or mod reduction when output needs truncation only.

20. Maximum Toys: Longest Consecutive Toys Within Budget

Problem

Given an array of toy prices (positive) and money, find longest contiguous subarray with sum <= money.

Approach

Use a two-pointer sliding window for nonnegative prices. Expand right pointer increasing sum; while sum > money move left pointer.

Solution (Python)

```python
def max_toys(prices, money):
left = 0
cur = 0
best = 0
for right, p in enumerate(prices):
cur += p
while cur > money and left <= right:
cur -= prices[left]
left += 1
best = max(best, right - left + 1)
return best
```

Explanation and Complexity

Sliding-window yields O(n). This is a standard interview pattern for longest subarray with bound on sum when elements are nonnegative.

21. Maximum Attendance: Longest Consecutive All-Present Days

Problem

Given m students and n strings for days (each string length m with 'P'/'A'), return the maximum run of consecutive days where all m students are present.

Approach

Build the target string 'P'*m. Iterate days and count consecutive equal-to-target runs, track maximum.

Solution (Python)

```python
def max_consecutive_all_present(m, days):
target = 'P' * m
cur = 0
best = 0
for d in days:
if d == target:
cur += 1
if cur > best: best = cur
else:
cur = 0
return best
```

Explanation and complexity

Linear scan O(n * m) due to string comparisons. Use this when checking group-wide conditions across day records.

22. Solve Equations: Apply T Expressions to an Array Repeatedly

Problem

Given an array and T text equations in variable x applied sequentially, evaluate each expression for every element and update the array each time.

Approach

For safe execution, constrain builtins; use eval with controlled globals where only math operations and x are available. Evaluate elementwise per expression.

Solution (Python)

```python
import math

def apply_expressions(arr, exprs):
# safe eval environment
safe_globals = {"__builtins__": None, "math": math}
for ex in exprs:
# precompile expression into lambda for speed
code = compile(ex, "<string>", "eval")
arr = [eval(code, safe_globals, {"x": x}) for x in arr]
return arr
```

Explanation and complexity

Each expression is evaluated for every element O(n * T). For interviews, discuss parsing or AST-based safe evaluation if eval is disallowed.

23. Lighthouse Clusters: Longest Run of Adjacent Positions With Diff ≤ 1

Problem

Given n integer positions of lighthouses, find the maximum count of consecutive positions where the adjacent absolute difference is 0 or 1 after sorting.

Approach

Sort positions and find the longest contiguous subsequence such that each adjacent difference <= 1.

Solution (Python)

```python
def max_cluster(arr):
if not arr: return 0
arr.sort()
cur = 1
best = 1
for i in range(1, len(arr)):
if abs(arr[i] - arr[i-1]) <= 1:
cur += 1
else:
if cur > best: best = cur
cur = 1
if cur > best: best = cur
return best
```

Explanation and Complexity

Sorting cost O(n log n), then single pass O(n). This is common for grouping numeric coordinates with constraints on gaps.

24. Match: How Many Team A Wins Less Than or Equal to Team B Wins per Match

Problem

Given arrays ar (team A wins per match) and br (team B wins per match), for each b in br compute count of a in ar with a <= b.

Approach

Sort ar and use binary search (bisect_right) for each b to count <= b.

Solution (Python)

```python
import bisect

def count_less_equal(ar, br):
ar_sorted = sorted(ar)
return [bisect.bisect_right(ar_sorted, b) for b in br]
```

Explanation and complexity

Sorting ar O(n log n) then each query O(log n). Use this pattern when answering many threshold queries over a static array.

25. Jumble the Words: Interleave Two Strings

Problem

Given two strings a and b, build a new string by alternatively taking one character from a then one from b, preserving internal order. If one string finishes, append the remaining tail of the other.

Approach

Iterate up to min(len(a), len(b)), append pairs, then append leftover slice.

Solution (Python)

```python
def jumble(a, b):
m = min(len(a), len(b))
parts = []
for i in range(m):
parts.append(a[i])
parts.append(b[i])
if len(a) > m:
parts.append(a[m:])
elif len(b) > m:
parts.append(b[m:])
return ''.join(parts)
```

Explanation and complexity

This is O(len(a)+len(b)) and preserves subsequence order. It tests basic string manipulation and sequence merging often seen in string problems.

7 More JavaScript Interview Questions Every Developer Should Know

1. Reverse Words in a Sentence: Fast, Safe, and Two Practical Implementations

Write reverseWords(sentence) that returns the words in reverse order. Input is a string of alphanumeric characters and spaces, no leading or trailing spaces, and at least one word.

Reasoning and Approach

A HackerRank-style coding challenge expects correctness, edge case handling, and attention to time and space complexity. The easiest, reliable approach uses split, reverse, and join. That is O(n) time and O(n) space, where n is the string length. For huge strings or environments where you want to avoid building intermediate arrays, a manual parse or an in-place reversal on a char array is an alternative. Validate input and avoid mutating original values passed by reference where possible.

Answer and Best Practices

Simple, readable solution (preferred for interview submissions and online judges):
function reverseWords(sentence) {
if (typeof sentence !== 'string') throw new TypeError('sentence must be a string');
// given constraints: no leading/trailing spaces
return sentence.split(' ').reverse().join(' ');
}

Time complexity: O(n). Space complexity: O(n). This is clear, concise, and passes typical test cases on coding platforms.
In-place style (works when you want to minimize intermediate arrays; strings are immutable in JS so we use char array):

Time complexity: O(n). Extra space: O(n) because of char array; useful for demonstrating algorithmic thinking on coding interview questions.

Edge Cases to Test

Single word
Two words
Repeated spaces are not allowed by problem constraints, but test what happens if present
Very long sentence for performance

Keywords: coding challenge, test cases, time complexity, space complexity, HackerRank style.

2. Find the Nth Largest Element in an Array: Sort vs Quickselect for Scale

Write findNthLargest(numbers, n) to return the nth most significant element. Array has at least n elements, may have duplicates, and can contain negatives.

Reasoning and Approach

A common interview solution sorts descending and picks index n-1. That is simple and acceptable for many HackerRank-style problems. For large inputs or when n is small, Quickselect gives average O(n) time and O(1) extra space and is a stronger algorithmic answer to show in interviews. Always avoid mutating caller data unless specified; clone the array before sorting.

Answer and Best Practices

Simple canonical solution (clear, safe, suitable for online judge):

function findNthLargest(numbers, n) {
if (!Array.isArray(numbers)) throw new TypeError('numbers must be an array');
if (n <= 0 || n > numbers.length) throw new RangeError('n out of range');
const copy = numbers.slice(); // avoid mutating original
copy.sort((a, b) => b - a);
return copy[n - 1];
}

Time complexity: O(m log m) where m = numbers.length. Space: O(m) due to slice.

Quickselect implementation (shows algorithmic depth):

Average time complexity: O(m). Good to mention when interviewing for performance sensitive roles.

Edge cases and test ideas

Duplicates
Negative and positive mix
n = 1 and n = length

Keywords: algorithm, coding interview, Hackerrank Interview Questions, time complexity, selection algorithm.

3. Implementing a Linked List: Node Structure, Operations, and Iteration

Implement a LinkedList with insert(value) at end, delete(value) first occurrence, and search(value) boolean.

Reasoning and Approach

For data structure coding problems, implement robust methods, maintain head and optional tail for O(1) append, and expose clear iteration. Use class syntax to show prototype inheritance awareness; classes in JavaScript use prototypes under the hood, so you can discuss prototype chain in interviews.

Answer and Best Practices

class Node {
constructor(value) {
this.value = value;
this.next = null;
}
}

class LinkedList {
constructor() {
this.head = null;
this.tail = null; // keep tail to make insert O(1)
this.length = 0;
}

insert(value) {
const node = new Node(value);
if (!this.head) {
this.head = this.tail = node;
} else {
this.tail.next = node;
this.tail = node;
}
this.length++;
}

delete(value) {
if (!this.head) return false;
if (this.head.value === value) {
this.head = this.head.next;
if (!this.head) this.tail = null;
this.length--;
return true;
}
let prev = this.head;
let cur = this.head.next;
while (cur) {
if (cur.value === value) {
prev.next = cur.next;
if (cur === this.tail) this.tail = prev;
this.length--;
return true;
}
prev = cur;
cur = cur.next;
}
return false;
}

search(value) {
let cur = this.head;
while (cur) {
if (cur.value === value) return true;
cur = cur.next;
}
return false;
}

Optional: iterate values
*values() {
let cur = this.head;
while (cur) {
yield cur.value;
cur = cur.next;
}
}
}

Usage and Complexity

Insert: O(1) with tail, delete: O(n) worst case, search: O(n). length kept for quick size checks.

Prototype inheritance note

The class methods live on the prototype, so instances share the same function references. In an interview, you can expand by adding methods to LinkedList.prototype directly to show understanding of the prototype chain.

Test cases

Insert multiple values then delete head, middle, tail
Delete missing value
Search existing and missing values

Keywords: data structures, coding challenge, HackerRank style, online judge, time complexity.

4. Implementing a Class Hierarchy with Inheritance: Shape, Rectangle, Circle, and prototype chain explained

Create base Shape with name and area() abstract method; create Rectangle and Circle subclasses implementing area.

Reasoning and Approach

Show clear OOP design, input validation, and use of prototype inheritance via class syntax. For interview quality code, throw a runtime error if the abstract method is not implemented, validate numeric inputs, and avoid silent failures.

Answer and Best Practices

class Shape {
constructor(name) {
if (typeof name !== 'string') throw new TypeError('name must be a string');
this.name = name;
}

area() {
throw new Error('area() must be implemented by subclass');
}
}

class Rectangle extends Shape {
constructor(width, height) {
super('Rectangle');
if (typeof width !== 'number' || typeof height !== 'number') {
throw new TypeError('width and height must be numbers');
}
this.width = width;
this.height = height;
}

area() {
return this.width * this.height;
}
}

class Circle extends Shape {
constructor(radius) {
super('Circle');
if (typeof radius !== 'number') throw new TypeError('radius must be a number');
this.radius = radius;
}

area() {
return Math.PI * this.radius * this.radius;
}
}

Prototype Chain and Notes

Instances created by new have constructors on their prototype; methods defined in class are placed on the prototype object. You can inspect via Object.getPrototypeOf(inst).area. For robust code, validate inputs and throw clear errors rather than returning NaN silently; this produces clearer test failures in online judge runs.

Complexity

Area calculations are constant time O(1). These patterns satisfy object oriented questions in coding interviews and HackerRank style problems.Keywords: class inheritance, prototype, coding interview, practice problems, test cases.

5. Finding the Longest Substring Without Repeating Characters: Sliding Window With Map

Write findLongestSubstring(str) that returns length of the longest substring without repeating characters.

Reasoning and Approach

Use sliding window plus a map (or object) to track last seen index of each character. This yields O(n) time complexity with O(min(n, alphabet)) space. For full correctness in production code consider Unicode code points; for HackerRank style inputs the standard char indexing is usually acceptable.

Answer and Best Practices

Edge Cases and Tests

Empty string returns 0
String with all identical characters
Mixed case sensitivity tests
Symbols and spaces if allowed by inputs

Keywords: sliding window, algorithm, HackerRank Interview Questions, time complexity, space complexity.

6. Sum of Two Numbers in an Array: Hash Map Primary, Two Pointers Alternative

Implement findSumOfTwo(arr, target) that returns two numbers summing to target or [] if none.

Reasoning and Approach

A hash map lookup yields O(n) time and O(n) space and is the standard for coding interviews. If the array may be sorted or you can sort it, a two pointer approach works in O(n log n) due to sorting and O(1) extra space.

Answer and Best Practices

Hash map approach (preferred for unsorted arrays)
function findSumOfTwo(arr, target) {
if (!Array.isArray(arr)) throw new TypeError('arr must be an array');
const map = new Map();
for (let i = 0; i < arr.length; i++) {
const num = arr[i];
const complement = target - num;
if (map.has(complement)) {
return [complement, num];
}
// store number to index or boolean; store index if needed for positions
map.set(num, i);
}
return [];
}

Time complexity: O(n). Space: O(n).

Two pointer approach if you may sort and returning values (not indices)
function findSumOfTwoSorted(arr, target) {
const a = arr.slice().sort((x, y) => x - y);
let l = 0, r = a.length - 1;
while (l < r) {
const s = a[l] + a[r];
if (s === target) return [a[l], a[r]];
if (s < target) l++;
else r--;
}
return [];
}

Edge Cases

Empty array
Duplicates
Negative numbers
Multiple valid pairs; either solution returns one pair consistent with constraints

Keywords: hash map, two pointers, coding challenge, test cases, online judge.

7. Working With Asynchronous JavaScript and Callbacks: Callbacks, Promises, Async/Await, and Event Loop Behavior

Write fetchData(callback) that simulates an async API call and calls callback with retrieved data.

Reasoning and Approach

Show an error-first callback pattern, then modernize to Promise and async/await for clearer control flow. Explain event loop ordering: setTimeout queues a macrotask; Promise resolution runs in microtask queue which executes before next macrotask, which matters when you chain async operations. For HackerRank or coding interview questions, illustrate knowledge of callback to Promise migration and proper error handling.

Answer and Best Practices

Callback style with error-first convention
function fetchData(callback) {
// simulate async fetch
setTimeout(() => {
try {
const data = { name: 'John', age: 28, city: 'New York' };
callback(null, data);
} catch (err) {
callback(err);
}
}, 2000);
}

Usage with error-first callback
function handleData(err, data) {
if (err) {
console.error('Fetch error', err);
return;
}
console.log('Received data:', data);
}
fetchData(handleData);

Best practice: use error-first callback signature so callers can handle errors predictably.

Converting to Promise and Async/await

Event Loop Note for Interviews

Promise callbacks (then/await) schedule microtasks which run before the next macrotask such as setTimeout callbacks. If you log order of synchronous code, microtasks, and macrotasks you can demonstrate precise knowledge of JavaScript concurrency behavior.

Testing and Edge Cases

Simulate network failures and timeouts.
Use AbortController or a timeout wrapper around Promises for cancellation in real cod.e

Keywords: asynchronous, callback, Promise, async await, event loop, microtask, macrotask, HackerRank style, coding interview, error handling.

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Is HackerRank Used for Interviews?

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Types of Skills HackerRank Evaluates

Stages in Which HackerRank Is Typically Used

Examples of Industries and Companies That Rely on HackerRank

Advantages of HackerRank

Potential Drawbacks and Limitations

Why HackerRank Is Beneficial for Coding Interview Preparation

Structured Learning Paths on HackerRank

Realistic Interview Simulation Features

Detailed Solutions and Explanations

Company Specific Preparation

Certifications and Skill Assessments

Community and Support

Accessibility and User Experience

Considerations When Using HackerRank

Tips for Using HackerRank Effectively

Familiar Candidate Mistakes on HackerRank Assessments

How Hiring Teams Use Report Data From HackerRank

Anti-Cheat and Integrity Features

Preparing for Company-Specific HackerRank Interview Questions

Live Interview Use With CodePair

Balancing Automated Tests With Human Interviews

Costs and Access

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Top 25 HackerRank Interview Questions and Answers

1. Maximum Passengers: Maximize Pickups on a Blocked Grid Round Trip

Problem

Approach

Solution (Python)

Explanation and Complexity

2. Minimum Street Lights: Cover a 1-D Road With Minimum Lights

Problem

Approach

Solution (Python)

Explanation and Complexity

3. Maximize Earnings: Weighted Interval Scheduling for Jobs

Problem

Approach

Solution (Python)

Explanation and complexity

4. Network Stream: Largest Repackaged Power-of-Two Chunk

Problem

Approach

Solution (Python)

Explanation and Complexity

5. Astronomy Lecture: Print Trapezium Star-Dot Pattern

Problem

Approach

Solution (Python)

Explanation and Complexity

6. Disk Space Analysis: maximum of sliding-window minimums

Problem

Approach

Solution (Python)

Explanation and Complexity

7. Guess the Word: First Maximum Odd-Length Word

Problem

Approach

Solution (Python)

Explanation and Complexity

8. Minimum Start Value: Keep Running Total ≥ 1

Problem

Approach

Solution (Python)

Explanation and Complexity

9. Complex Math: Chain Additions of Complex Numbers

Problem

Approach

Solution (Python)

Explanation and Complexity

10. Minimum Occurrence: Least Frequent Character With Earliest Tie-Break

Problem

Approach

Solution (Python)

Explanation and Complexity

11. Devil Groups: Maximum Group of People Left of Devils

Problem