Coding Interview Preparation
رفتن به کانال در Telegram
Coding interview preparation for software engineers Interview questions, DSA, clean solutions. Join 👉 https://rebrand.ly/bigdatachannels Buy ads: https://telega.io/c/coding_interview_preparation DMCA: @disclosure_bds Contact: @mldatascientist
نمایش بیشتر
شبکه:5 894
مشترکین
+424 ساعت
+177 روز
-430 روز
در حال بارگیری داده...
کانالهای مشابه
ابر برچسبها
اشارات ورودی و خروجی
---
---
---
---
---
---
جذب مشترکین
اوت '26
اوت '26
+83
در 0 کانالها
ژوئیه '26
+112
در 0 کانالها
Get PRO
ژوئن '26
+114
در 2 کانالها
Get PRO
مه '26
+111
در 1 کانالها
Get PRO
آوریل '26
+113
در 2 کانالها
Get PRO
مارس '26
+65
در 2 کانالها
Get PRO
فوریه '26
+82
در 0 کانالها
Get PRO
ژانویه '26
+90
در 9 کانالها
Get PRO
دسامبر '25
+65
در 1 کانالها
Get PRO
نوامبر '25
+66
در 1 کانالها
Get PRO
اکتبر '25
+55
در 2 کانالها
Get PRO
سپتامبر '25
+69
در 0 کانالها
Get PRO
اوت '25
+100
در 0 کانالها
Get PRO
ژوئیه '25
+97
در 0 کانالها
Get PRO
ژوئن '25
+79
در 0 کانالها
Get PRO
مه '25
+61
در 1 کانالها
Get PRO
آوریل '25
+61
در 0 کانالها
Get PRO
مارس '25
+67
در 0 کانالها
Get PRO
فوریه '25
+77
در 1 کانالها
Get PRO
ژانویه '25
+117
در 1 کانالها
Get PRO
دسامبر '24
+165
در 0 کانالها
Get PRO
نوامبر '24
+80
در 1 کانالها
Get PRO
اکتبر '24
+86
در 0 کانالها
Get PRO
سپتامبر '24
+71
در 0 کانالها
Get PRO
اوت '24
+83
در 0 کانالها
Get PRO
ژوئیه '24
+153
در 0 کانالها
Get PRO
ژوئن '24
+203
در 0 کانالها
Get PRO
مه '24
+205
در 0 کانالها
Get PRO
آوریل '24
+187
در 1 کانالها
Get PRO
مارس '24
+259
در 0 کانالها
Get PRO
فوریه '24
+286
در 0 کانالها
Get PRO
ژانویه '24
+391
در 0 کانالها
Get PRO
دسامبر '23
+322
در 0 کانالها
Get PRO
نوامبر '23
+50
در 0 کانالها
Get PRO
اکتبر '23
+54
در 0 کانالها
Get PRO
سپتامبر '23
+88
در 0 کانالها
Get PRO
اوت '23
+146
در 0 کانالها
Get PRO
ژوئیه '23
+195
در 0 کانالها
Get PRO
ژوئن '23
+127
در 0 کانالها
Get PRO
مه '23
+119
در 0 کانالها
Get PRO
آوریل '23
+129
در 0 کانالها
Get PRO
مارس '23
+134
در 0 کانالها
Get PRO
فوریه '23
+126
در 0 کانالها
Get PRO
ژانویه '23
+121
در 0 کانالها
Get PRO
دسامبر '22
+217
در 0 کانالها
Get PRO
نوامبر '22
+110
در 0 کانالها
Get PRO
اکتبر '22
+204
در 0 کانالها
Get PRO
سپتامبر '22
+273
در 0 کانالها
Get PRO
اوت '22
+143
در 0 کانالها
Get PRO
ژوئیه '22
+128
در 0 کانالها
Get PRO
ژوئن '22
+164
در 0 کانالها
Get PRO
مه '22
+159
در 0 کانالها
Get PRO
آوریل '22
+164
در 0 کانالها
Get PRO
مارس '22
+356
در 0 کانالها
Get PRO
فوریه '22
+150
در 0 کانالها
Get PRO
ژانویه '22
+83
در 0 کانالها
Get PRO
دسامبر '21
+32
در 0 کانالها
Get PRO
نوامبر '21
+20
در 0 کانالها
Get PRO
اکتبر '21
+32
در 0 کانالها
Get PRO
سپتامبر '21
+232
در 0 کانالها
Get PRO
اوت '21
+1 029
در 0 کانالها
| تاریخ | رشد مشترکین | اشارات | کانالها | |
| 26 اوت | +1 | |||
| 25 اوت | +4 | |||
| 24 اوت | +3 | |||
| 23 اوت | +6 | |||
| 22 اوت | +3 | |||
| 21 اوت | +5 | |||
| 20 اوت | +5 | |||
| 19 اوت | +3 | |||
| 18 اوت | +1 | |||
| 17 اوت | +1 | |||
| 16 اوت | +2 | |||
| 15 اوت | 0 | |||
| 14 اوت | +3 | |||
| 13 اوت | +3 | |||
| 12 اوت | +3 | |||
| 11 اوت | +1 | |||
| 10 اوت | +1 | |||
| 09 اوت | +7 | |||
| 08 اوت | +10 | |||
| 07 اوت | +5 | |||
| 06 اوت | +6 | |||
| 05 اوت | +1 | |||
| 04 اوت | +3 | |||
| 03 اوت | 0 | |||
| 02 اوت | +2 | |||
| 01 اوت | +4 |
پستهای کانال
💬 MOTIVATIONAL / DISCUSSION - The Uncomfortable Truth About Rejections
Here's something senior engineers rarely say out loud: almost everyone gets rejected by companies they were genuinely qualified for. Not because they were bad candidates - because interviewing has enormous variance. A different interviewer, a slightly different question, a slightly off day, and the same person gets a completely different outcome.
This isn't meant to lower the bar - it's meant to correct a mental model that causes real damage: treating every single rejection as objective proof you're "not good enough."
The engineers who eventually land great offers aren't the ones who never get rejected. They're the ones who treat each rejection as one data point, extract whatever's actually learnable from it (was there a real skill gap, or was it just variance?), and keep going.
If you're in the middle of a job search right now and it's been rough - you're not alone, and it's not necessarily a reflection of your actual ability.
What's one thing that's kept you going during a tough job search? Let's hear it 👇
| 2 | 📊 SQL SATURDAY #7 (BONUS) - NULLs: The Silent Query Killer
NULL doesn't behave like a normal value, and it quietly breaks queries that "look" correct. This trips up even experienced engineers.
customers
+----+---------+---------+
| id | name | phone |
+----+---------+---------+
| 1 | Alice | 555-1234|
| 2 | Bob | NULL |
| 3 | Charlie | NULL |
⚠️ Trap #1: WHERE phone = NULL returns ZERO rows - always. NULL means "unknown," and "is unknown equal to unknown?" is itself unknown, not true. You must use IS NULL:
sql
SELECT * FROM customers WHERE phone IS NULL; -- ✅ correct
⚠️ Trap #2: COUNT(phone) vs COUNT(*) give different results. COUNT(*) counts all rows; COUNT(column) only counts non-NULL values in that column.
sql
SELECT COUNT(*) FROM customers; -- 3
SELECT COUNT(phone) FROM customers; -- 1
⚠️ Trap #3: NULL values are often silently EXCLUDED from aggregate calculations in ways people don't expect:
sql
SELECT AVG(phone_call_count) FROM customers;
-- NULLs are ignored entirely, NOT treated as 0.
-- If you wanted them treated as 0, use:
SELECT AVG(COALESCE(phone_call_count, 0)) FROM customers;
COALESCE(value, default) returns the first non-NULL argument - extremely useful for handling missing data gracefully instead of letting it silently skew your results.
Has a NULL-related bug ever quietly thrown off a real report at your job? 👇 | 94 |
| 3 | 📄 RESUME ROAST #6 - Bonus Round: The Objective Statement
> "Objective: To obtain a challenging position in a dynamic company where I can utilize my skills and grow professionally while contributing to organizational success."
This one's almost a meme at this point. What's wrong? 👇
.
.
.
The roast: This sentence could be copy-pasted onto literally any resume, for literally any job, in any industry, and nobody would notice. It says absolutely nothing specific about you, and it wastes prime real estate - the very TOP of your resume, the part guaranteed to get read.
Objective statements are largely considered outdated in software engineering resumes. Recruiters already know your objective is "get this job" - you don't need to state it.
✅ Replace it with a brief, specific summary (optional, and only if it adds real value):
> "Backend engineer with 4 years building high-throughput payment systems in Python and Go; specialized in reducing latency at scale."
This tells a recruiter, in one line, exactly what box to file you in and why they should keep reading - which is the entire job of the first line of your resume.
If your resume still has an "Objective" section, that might be worth revisiting today. Does yours? 👇 | 118 |
| 4 | 🧠 EDUCATIONAL CS #5 - Concurrency: Race Conditions vs. Deadlocks
Two concurrency terms that get confused constantly - here's the clean distinction.
🔹 Race condition: the outcome depends on unpredictable TIMING of operations. We saw this back in Spot the Bug #2 - two threads incrementing a shared counter, and depending on exact timing, updates get lost.
🔹 Deadlock: two or more threads are permanently stuck, each waiting for a resource the other one holds, forever.
Thread A: holds Lock 1, waiting for Lock 2
Thread B: holds Lock 2, waiting for Lock 1
→ Neither can ever proceed. Frozen forever.
The classic fix for deadlocks: always acquire locks in a consistent, agreed-upon order across your entire codebase. If EVERY thread always locks Resource 1 before Resource 2 (never the reverse), the circular waiting pattern above becomes structurally impossible.
Why this matters in interviews: system design and backend interviews increasingly probe concurrency understanding, even outside dedicated "concurrency" questions - e.g., "what happens if two requests try to update the same row at the same time?" is really asking about race conditions, often expecting you to mention database-level solutions like row locking or optimistic concurrency control (checking a version number before committing a write).
Race condition or deadlock - which one is scarier to debug in your experience, and why? 👇 | 131 |
| 5 | 🗣️ BEHAVIORAL INTERVIEW #5 - "Tell Me About a Time You Disagreed With a Decision"
Similar to the conflict question, but this one specifically probes: can you push back on authority respectfully, and can you also let go gracefully if you don't get your way?
✅ The structure that works:
1. What was the decision, and why did you disagree?
2. How did you raise your concern (privately? with data? at the right time?)
3. What was the outcome - did they change course, or did you disagree and commit?
4. How did you handle it either way?
Example:
"My manager decided to launch a feature without A/B testing it first, to hit a deadline. I disagreed, so I put together a quick doc showing the risk based on a similar past launch that had gone poorly without testing. He read it, but ultimately decided the deadline pressure from a client commitment outweighed the risk. I made sure my concerns were documented, then fully committed to making the launch as smooth as possible - added extra monitoring and a fast rollback plan just in case. It ended up working out fine, but even if it hadn't, I'd rather have raised the concern clearly once than either stayed silent or kept relitigating it after the decision was made."
This shows: you can think critically and push back, you use evidence rather than just opinion, and - critically - you know how to "disagree and commit" once a decision is made, which is a trait senior leaders explicitly look for.
Have you ever disagreed with a decision and had to commit to it anyway? How'd that feel? 👇 | 133 |
| 6 | 📚How I’d Prepare for an IT Interview Before I Even Applied
If you're applying for technical roles, don't prepare for interviews and certifications as two completely separate things.
Use them together.
1️⃣ Start with the role
🔹 roadmap.sh
Pick your path and identify the technologies you actually need.
Data Engineer, DevOps, Cloud, Cybersecurity, Backend, etc.
2️⃣ Find the questions you'll actually face
🔹 IT Interview Questions
It has role-specific collections across Cloud, Data/BI, Cybersecurity, Software/DevOps and many more.
Don't memorize the answers. Use the questions to find what you don't understand.
3️⃣ Go deeper on weak areas
Some of the best free technical material isn't packaged as "interview prep."
🔹 MIT Missing Semester
🔹 Google SRE Books
🔹 Full Stack Deep Learning
🔹 DataTalksClub
These are the kind of resources worth keeping long after the interview.
4️⃣ If the role requires cloud, prepare for the certification side too
This is where certification material becomes useful even if you aren't planning to take the exam.
🔹 Free training & study materials
This is a platform to learn different materials including AWS, Microsoft, CompTIA and other cloud/IT certification resources.
And always keep the official documentation nearby:
AWS
Azure
Google Cloud
5️⃣ If You Need actual certification practice
🔹 Exam practice
Use this specifically when you're preparing for a certification. I wouldn't use it as an interview-question substitute.
For official practice, also check:
🔹 AWS Skill Builder
🔹 Microsoft Learn
6️⃣ The final test
Close everything. Pick 10 questions. Answer them out loud.
Then take one architecture problem and explain:
requirements → architecture → trade-offs → failure scenarios → scaling → cost
If you can't explain your answer without looking something up, you've found what to study next.
Don't collect resources. Build a preparation loop:
Learn → practice → identify gaps → study → explain → repeat.
That's what actually gets you interview-ready. | 129 |
| 7 | 🎯 CODING CHALLENGE #10 - Word Ladder (BFS Shortest Path)
Difficulty: Hard | Asked at: Google, Amazon, LinkedIn
Given beginWord, endWord, and a word list, find the length of the shortest transformation sequence, changing one letter at a time, where each intermediate word must exist in the word list.
Input: beginWord = "hit", endWord = "cog"
wordList = ["hot","dot","dog","lot","log","cog"]
Output: 5 ("hit" -> "hot" -> "dot" -> "dog" -> "cog")
💡 Hint: "Shortest transformation" is your biggest clue - this is shortest path in an unweighted graph, which means BFS, not DFS.
Solution:
python
from collections import deque
def ladder_length(begin_word, end_word, word_list):
word_set = set(word_list)
if end_word not in word_set:
return 0
queue = deque([(begin_word, 1)])
visited = {begin_word}
while queue:
word, steps = queue.popleft()
if word == end_word:
return steps
for i in range(len(word)):
for c in 'abcdefghijklmnopqrstuvwxyz':
next_word = word[:i] + c + word[i+1:]
if next_word in word_set and next_word not in visited:
visited.add(next_word)
queue.append((next_word, steps + 1))
return 0
Complexity: O(M² × N) time, where M is word length and N is the word list size - for each word, we try M positions × 26 letters, each generating an M-length string.
Common mistake: Reaching for DFS because it "feels" more natural for pathfinding - DFS can find A path, but has no guarantee it finds the SHORTEST one without exploring everything. The moment you see "shortest" or "minimum steps" in unweighted graph problems, that's your signal: BFS.
This is one of those problems where recognizing the underlying pattern (shortest path = BFS) matters far more than clever tricks - the "graph" here isn't even given to you explicitly, you have to realize that words are nodes and one-letter-difference is an edge.
Did you recognize this as a graph problem right away, or did it take a second to see it? 👇 | 167 |
| 8 | ⚠️ COMMON INTERVIEW MISTAKE #5 - Not Asking ANY Questions at the End
"Do you have any questions for me?" is not a courtesy formality - interviewers actively judge your answer to this, and "no, I think you covered everything" is a genuinely weak close to an interview.
Why it matters: it's your best remaining signal-generating opportunity, AND it shows genuine engagement rather than someone just trying to get through the process.
✅ Strong questions to have ready (tailor to who you're talking to):
To an engineer: "What's the most painful part of the codebase right now, and is there a plan to address it?"
To a manager: "How do you measure success for someone in this role after 6 months?"
To anyone: "What's something about working here that surprised you, in either a good or bad way?"
❌ Avoid questions Google can already answer (basic company facts, publicly available info) - it signals you didn't do basic homework.
❌ Avoid ONLY asking about perks/benefits/PTO in the first interview - save that for later rounds or the recruiter; asking it too early to your potential future teammates can look like priorities are misplaced.
Save at least 2-3 genuinely good questions for every interview. It's one of the highest-leverage, lowest-effort improvements you can make.
What's the best question you've ever asked (or been asked) at the end of an interview? 👇 | 176 |
| 9 | 🔍 GUESS THE OUTPUT #6 - full breakdown
python
def outer():
x = 10
def inner():
print(x)
x += 1
inner()
outer()
Answer: UnboundLocalError: local variable 'x' referenced before assignment
This one surprises a lot of people - it does NOT print 10. Here's why: because inner() assigns to x (x += 1), Python treats x as a LOCAL variable throughout the entire function the moment it sees any assignment to it, even before that line executes. So the print(x) line is trying to read a local x that hasn't been assigned yet.
Fixed version:
python
def outer():
x = 10
def inner():
nonlocal x
print(x)
x += 1
inner()
The nonlocal keyword tells Python "this x refers to the enclosing function's variable, not a new local one."
This is a genuinely tricky one - Python decides variable scope at compile time based on whether a name is assigned anywhere in the function, not based on execution order. Great interview question for testing real understanding of closures vs. just pattern-matching syntax. | 163 |
| 10 | What happens when outer() runs? | 209 |
| 11 | 🔍 GUESS THE OUTPUT #6
Language: Python
python
def outer():
x = 10
def inner():
print(x)
x += 1
inner()
outer()
Lock in your answer, then vote on the quiz below 👇 | 196 |
| 12 | 🏗️ SYSTEM DESIGN MONDAY #6 - Let's Design Instagram (Feed + Media at Scale)
Bigger system this week. Core requirements:
✅ Users post photos/videos
✅ Users follow other users
✅ Users see a feed of posts from people they follow
✅ Massive read traffic - feeds are viewed constantly, posts created far less often
The hardest question in this whole design: how do you GENERATE the feed?
🔹 Option A - Pull model (fan-out on read): When a user opens their feed, query posts from everyone they follow, merge, and sort by time.
[User requests feed] → [Query posts from all 500 people they follow] → [Merge + sort] → [Return]
Simple, but SLOW for users following many accounts - that query fans out wide every single time they open the app.
🔹 Option B - Push model (fan-out on write): When someone posts, immediately push that post into the precomputed feed of every follower.
[User posts] → [Push post into feed_cache for each of their 10K followers]
Feed reads become instant (just read your precomputed feed) - but this completely breaks down for celebrities with 50 million followers, since one post would mean 50 million writes.
✅ The real answer (hybrid): Push model for regular users, pull model for accounts above a follower threshold (celebrities). Merge the two at read time. This exact hybrid approach is used by real large-scale social platforms, and mentioning it shows you understand that there's rarely one clean solution at true scale - just the least-bad tradeoff for each case.
Media storage: Photos/videos themselves don't belong in your database - they go in object storage (like S3), with only the URL/reference stored in the database. A CDN (Content Delivery Network) then caches that media geographically close to users, so someone in Tokyo isn't fetching an image from a US-based server every time.
[App Server] → stores reference → [Database]
[App Server] → stores actual file → [Object Storage] → cached globally by → [CDN]
Which feed generation approach would you have guessed first, before reading the hybrid answer? 👇 | 223 |
| 13 | +1 Cloud Computing Notes
One of our memebers asked for this👆 | 216 |
| 14 | 📄 RESUME ROAST #5
> "Software Engineer | Company XYZ | 2019 - Present
> • Wrote code for various projects
> • Fixed bugs
> • Attended daily standups
> • Participated in code reviews"
Roast time - what's the core problem across ALL four bullets? 👇
.
.
.
The roast: Every single bullet describes an ACTIVITY, not an OUTCOME. "Attended daily standups" is something literally everyone on the team does - it says nothing about YOUR specific contribution or impact, and takes up a bullet point that could've held something valuable.
The test for every resume bullet: could this exact sentence apply to any random person on the team, doing the bare minimum? If yes, cut it or rewrite it.
✅ Rewritten with the same underlying work, framed around outcomes:
> • Redesigned the checkout flow, reducing cart abandonment by 18%
> • Diagnosed and fixed a memory leak in the notification service, cutting server costs by $2K/month
> • Led code review standards adoption across a 6-person team, catching 30% more bugs pre-merge
Same person, same actual job - completely different resume. Numbers and outcomes aren't decoration, they're the entire point.
Look at your own resume - how many bullets describe activities vs. outcomes? Be brutally honest 👇 | 297 |
| 15 | 🎯 CODING CHALLENGE #9 - LRU Cache
Difficulty: Medium-Hard | Asked at: Amazon, Google, Meta
Design a Least Recently Used (LRU) cache with O(1) get and put operations.
cache = LRUCache(2)
cache.put(1, 1)
cache.put(2, 2)
cache.get(1) # returns 1, and marks 1 as recently used
cache.put(3, 3) # evicts key 2 (least recently used)
cache.get(2) # returns -1 (not found)
💡 Hint: This is the perfect combo problem - you need O(1) lookup (hash map) AND O(1) reordering/eviction (doubly linked list). Neither alone gets you there.
Solution:
python
class Node:
def __init__(self, key, val):
self.key = key
self.val = val
self.prev = None
self.next = None
class LRUCache:
def __init__(self, capacity):
self.capacity = capacity
self.cache = {}
self.head = Node(0, 0)
self.tail = Node(0, 0)
self.head.next = self.tail
self.tail.prev = self.head
def _remove(self, node):
node.prev.next = node.next
node.next.prev = node.prev
def _add_to_front(self, node):
node.next = self.head.next
node.prev = self.head
self.head.next.prev = node
self.head.next = node
def get(self, key):
if key not in self.cache:
return -1
node = self.cache[key]
self._remove(node)
self._add_to_front(node)
return node.val
def put(self, key, value):
if key in self.cache:
self._remove(self.cache[key])
node = Node(key, value)
self.cache[key] = node
self._add_to_front(node)
if len(self.cache) > self.capacity:
lru = self.tail.prev
self._remove(lru)
del self.cache[lru.key]
Complexity: O(1) time for both get and put, O(capacity) space.
Common mistake: Trying to use Python's built-in list to track recency order - list.remove() and re-inserting are O(n), which defeats the entire point. The doubly linked list is what makes removal from the middle O(1), because you don't need to search for the node - you already have a direct reference to it.
💡 Pro tip: in a real interview, mentioning OrderedDict in Python (which has built-in move_to_end()) is a great way to show you know the language deeply - but building it from scratch with a hash map + linked list is what interviewers actually want to see, since it proves you understand WHY it's O(1), not just that a library exists.
This is consistently rated one of the best "combines two data structures" interview questions. Have you built this before, or is this your first time seeing it? 👇 | 244 |
| 16 | 📊 SQL SATURDAY #6 - Self Joins and Hierarchies
employees
+----+---------+-----------+
| id | name | manager_id|
+----+---------+-----------+
| 1 | Alice | NULL |
| 2 | Bob | 1 |
| 3 | Charlie | 1 |
| 4 | Diana | 2 |
Question: List each employee alongside their manager's name.
sql
SELECT e.name AS employee, m.name AS manager
FROM employees e
LEFT JOIN employees m ON e.manager_id = m.id;
This is a self join - the same table joined to itself, aliased twice so SQL can treat it as two logical tables. Alice (the CEO) has manager_id = NULL, so she shows up with manager = NULL thanks to the LEFT JOIN.
⚠️ Interview follow-up: "Find all employees who earn more than their manager." This is a classic self-join + comparison question:
sql
SELECT e.name AS employee, e.salary, m.name AS manager, m.salary
FROM employees e
JOIN employees m ON e.manager_id = m.id
WHERE e.salary > m.salary;
Deeper follow-up: "Find the full management chain for a given employee, regardless of depth." A single self join can't do this - it only goes one level up. This needs a recursive CTE:
sql
WITH RECURSIVE chain AS (
SELECT id, name, manager_id
FROM employees
WHERE id = 4 -- start with Diana
UNION ALL
SELECT e.id, e.name, e.manager_id
FROM employees e
JOIN chain c ON e.id = c.manager_id
)
SELECT * FROM chain;
Recursive CTEs come up more than people expect for org charts, category trees, and dependency graphs. Worth practicing even if it feels unfamiliar at first.
Have you ever had to query an actual org chart or category tree at work? 👇 | 211 |
| 17 | 🐛 SPOT THE BUG #5
Language: SQL
sql
SELECT customer_id, COUNT(*) as order_count
FROM orders
WHERE order_date > '2024-01-01'
GROUP BY customer_id
ORDER BY order_count DESC
LIMIT 1;
Goal: find the customer with the most orders after Jan 1st, 2024.
Looks right at first glance - what's the subtle issue? 👇
.
.
.
The bug: LIMIT 1 silently drops any ties. If TWO customers are tied for the most orders, this query arbitrarily returns just one of them (and which one is returned isn't guaranteed to be consistent across database engines or even across runs).
If the actual requirement is "find ALL customers tied for the most orders," this query quietly gives a wrong (incomplete) answer that LOOKS correct.
Fixed version (handles ties):
sql
WITH ranked AS (
SELECT customer_id, COUNT(*) as order_count,
RANK() OVER (ORDER BY COUNT(*) DESC) as rnk
FROM orders
WHERE order_date > '2024-01-01'
GROUP BY customer_id
)
SELECT customer_id, order_count
FROM ranked
WHERE rnk = 1;
💡 This is a great example of why clarifying requirements matters even in SQL questions - "top 1" and "all customers tied for the top spot" are genuinely different problems, and a query that's correct for one is silently wrong for the other.
Have you ever shipped a query that "worked" but quietly handled ties incorrectly? 👇 | 225 |
| 18 | 🕵️ RECRUITER SECRETS #5 - The Truth About "Overqualified"
Ever been told you're "overqualified" for a role? Here's what's actually going on behind that phrase, because it's rarely about your skills:
🔹 Flight risk concern: they're worried you'll leave the moment something better comes along, wasting their onboarding investment.
🔹 Salary concern: they assume you'll ask for more than the role is budgeted for.
🔹 Team dynamics concern: they worry you'll be frustrated reporting to someone more junior, or bored by the scope of work.
✅ How to address it directly, if you actually want the role:
"I understand the concern - I'm specifically looking for [genuine reason: better work-life balance / a switch to a domain I'm passionate about / a smaller team where I have more ownership], and I'm fully aligned with the scope and comp for this level. I'm not looking at this as a stepping stone."
Naming the concern directly and addressing it head-on is far more effective than hoping it doesn't come up. Vague reassurance ("no really, I just want this job!") without addressing WHY they're worried usually doesn't land.
If it's genuinely not the right fit level for you, that's okay too - but going in with a real, honest answer to "why would you take a step back" beats dodging the question every time.
Has "overqualified" ever come up for you? How'd you handle it? 👇 | 213 |
| 19 | 🗣️ BEHAVIORAL INTERVIEW #4 - "Why Do You Want to Work Here?"
The laziest possible answer: "I've heard great things about the culture and I'm excited about the growth opportunities." Every interviewer has heard this exact sentence a thousand times, and it signals you didn't actually research the company.
✅ What separates a great answer:
1️⃣ Reference something SPECIFIC about the company - a product decision, an engineering blog post, a technical challenge unique to their scale.
2️⃣ Connect it to YOUR specific experience or interests - not generically "I'm passionate about tech."
3️⃣ Show you've thought about what you'd actually be doing day-to-day, not just the brand name.
Example:
"I read your engineering blog post about migrating from a monolith to microservices, and the challenges you described around data consistency really matched what I worked through in my current role, just at a much smaller scale. I'm excited about tackling that same problem at 10x the complexity, and learning from a team that's already been through it."
This takes 15 minutes of research beforehand and instantly puts you ahead of 80% of candidates who show up unprepared for this exact question.
Do you research the company's engineering blog before interviews? If not, put it on your prep checklist right now 😉 | 196 |
| 20 | 🎯 CODING CHALLENGE #8 - Course Schedule (Detect Cycle in a Graph)
Difficulty: Medium-Hard | Asked at: Google, Meta, Uber
You have numCourses courses, and a list of prerequisite pairs [a, b] meaning "to take course a, you must first take course b." Determine if it's possible to finish all courses (i.e., there's no cyclic dependency).
Input: numCourses = 2, prerequisites = [[1,0]]
Output: true
Input: numCourses = 2, prerequisites = [[1,0],[0,1]]
Output: false (cycle: 0 needs 1, 1 needs 0)
💡 Hint: This is cycle detection in a directed graph. Topological sort (Kahn's algorithm using in-degrees) is the cleanest approach.
Solution:
python
from collections import deque
def can_finish(num_courses, prerequisites):
graph = {i: [] for i in range(num_courses)}
in_degree = [0] * num_courses
for course, prereq in prerequisites:
graph[prereq].append(course)
in_degree[course] += 1
queue = deque([i for i in range(num_courses) if in_degree[i] == 0])
completed = 0
while queue:
node = queue.popleft()
completed += 1
for neighbor in graph[node]:
in_degree[neighbor] -= 1
if in_degree[neighbor] == 0:
queue.append(neighbor)
return completed == num_courses
Complexity: O(V + E) time and space, where V is courses and E is prerequisite pairs.
Common mistake: Trying to solve this with plain DFS + a visited set, without tracking the CURRENT recursion path separately. You need to distinguish "visited overall" from "visited in this current path" - otherwise you can't actually detect a cycle, only whether a node's been seen at all.
This "can this graph be finished/ordered" pattern (topological sort) shows up under many disguises - build systems, task scheduling, spreadsheet formula dependencies. Recognize the shape and you'll spot it fast.
Kahn's algorithm or DFS-based cycle detection - which do you find more intuitive? 👇 | 234 |
