Coding Interview Question Bank by Company: How to Build a Targeted Set

February 9, 2026

TL;DR

A “coding interview question bank by company” is only useful if it answers one question fast: what should I practice next for this company and role, given my current gaps? Build a targeted set by tagging questions with company, role, level, and (most importantly) pattern, then keep a status that forces action: attempt → redo → explain. Use company tags to prioritize selection, not to chase exact repeats. If you keep the bank pattern-first, you’ll get the best of both worlds: higher signal for your target and skills that still transfer when the interviewer changes the angle.

Introduction

Most candidates don’t have a “practice problem” problem. They have a selection problem.

They save ten company lists, open one at random, grind for an hour, and still feel unsure—because nothing connects yesterday’s work to today’s choice. A question bank fixes that, but only when it behaves like a tool, not a bookmark folder.

A good bank doesn’t motivate you. It reduces decision fatigue, surfaces the right patterns, and makes review unavoidable.

Why company-targeted banks work

Company targeting is valuable because it narrows the search space. When time is limited, practicing low-signal problems is the biggest waste.

But company targeting fails in predictable ways:

You treat a list like a promise (“they always ask this”).
You hunt for “exact repeats” instead of training patterns and follow-ups.
You keep collecting new questions because it feels like progress.

A targeted bank should do the opposite: it should make repetition feel normal, and make randomness feel suspicious.

If you want the speaking layer to stay consistent while the question changes, keep a reusable structure nearby: Explain Code in Interviews: Templates (2026). And if your follow-ups tend to derail you, pair that with a consistent reasoning habit for complexity: Big-O in Interviews: Natural Time/Space Script.

The minimum schema that actually stays useful

You don’t need a complicated system. You need the right fields.

Here’s a minimal schema that works in Notion, Sheets, Airtable, or any database:

Field	What it stores	Why it matters
Company	target company	prioritization and filtering
Role	backend / frontend / data / mobile	pattern weighting changes by role
Level	early / mid / senior	sets depth expectations
Pattern	sliding window, BFS/DFS, DP, etc.	prevents random grinding
Difficulty	easy / medium / hard	pacing and timeboxing
Source	where it came from	maintenance and trust
Recency	“recent”, “unknown”, “old”	reduces false confidence from stale sets
Status	new / attempted / redo / explained	drives your next action
Field notes	invariant, gotcha, edge cases	turns a question into a reusable lesson
Redo window	a future redo slot	creates retrieval instead of recognition

Two notes that make this system survive:

First, keep status action-based. “Solved” is not an action. “Redo” is.

Second, don’t over-precision recency. You don’t need perfect dates to benefit. A three-bucket label (“recent / unknown / old”) is usually enough to keep you honest.

How to fill the bank without over-trusting sources

Treat sources as inputs, not truth.

Common inputs:

curated community sets and study lists
role- or company-tagged collections
your own mock feedback and interview notes
question hubs that already group prompts by company and role

If you want a stable place to start building targeted sets by company and role, use the Interview Questions & Answers Hub. If you prefer a question-bank style pool you can reuse across weeks (instead of constantly hunting), pull from the IQB interview question bank and tag your own patterns and redo status.

Then do one thing that most people skip: normalize every entry into a pattern.

A company tag alone creates a scrolling list. A company tag plus a pattern tag creates a training plan.

Retrieval you’ll actually use when you’re tired

The value of a bank is not the database. It’s the retrieval.

If you can’t pull a sensible next set in ten seconds, you won’t use it when you’re busy.

Start with retrieval queries that match real prep moments:

“Company + Role + Status = redo”
“Company + Pattern = weak”
“Role + Pattern + Status = explained? no”
“Company + Difficulty = medium + status = attempted”

In practice, you want two “buttons”:

One button that gives you today’s work.

One button that gives you today’s review.

Everything else is optional.

When your retrieval is stable, your practice becomes predictable—and that’s when you stop feeling like you’re starting over every day.

The practice loop that makes the bank pay off

A bank decides what to practice. A loop decides whether you improve.

Keep the loop simple:

Attempt → test → redo from memory → explain out loud → repeat later

If you want a structured breakdown of that loop (and what to do when you fail), connect it to: Which List to Do: LeetCode75 vs Blind75 vs NeetCode150. And to prevent “I solved it but missed the trap,” pair it with edge-case habits: Coding Interview Edge Cases: 30 to Say Out Loud.

For the tool question—“can AI help without messing up my learning?”—anchor your policy here: AI for Coding Interview Practice: When It’s Reliable.

Field notes: what “field notes” looks like in real life

Here’s a real application pattern that shows why “field notes” is the highest ROI column in the whole bank.

Example prompt the candidate stored (company-tagged):

“A backend screen asked for an approach to detect duplicates within k distance (sliding window with hashing). Follow-up changed constraints: memory cap tightened.”

What went wrong on the first attempt:

They knew the pattern name, but their explanation fell apart when the follow-up arrived. They couldn’t say what broke, and they couldn’t defend space.

Field notes entry:

Invariant: “Window contains at most k recent elements; duplicates are checked within that window.”
Edge case: “k equals zero; window becomes empty every step.”
Worst case: “All elements unique, set grows to k.”
Complexity line: “Time linear in n; space grows with window size.”

How Beyz helped without taking over:

After the candidate attempted, they used Beyz Coding Assistant as a post-attempt reviewer: generate break tests, ask follow-ups, and challenge the complexity explanation. Then they rewrote the explanation using the same speaking template from Explain Code in Interviews: Templates (2026) and pinned the final “one-minute version” into the Field notes column.

What changed wasn’t the difficulty. It was that the lesson became searchable and reusable the next time “k-window + follow-up constraint shift” showed up.

Case replay: the mistake most people think is “not enough questions”

A candidate built a huge spreadsheet of company questions and still felt stuck. Their sessions were busy, but nothing felt owned.

When they looked closer, every entry was marked “done” after a single solve. There were no redos. No explanation notes. No follow-up practice. The bank wasn’t a bank—it was a trophy shelf.

They rebuilt the system around two rules:

No question gets “explained” status unless they can redo it from memory and narrate it cleanly.
Every entry must have a field note that includes an invariant and at least one edge case.

The set got smaller. Performance improved faster. Follow-ups stopped feeling like ambushes because they’d practiced the part that actually breaks people: explaining and adapting under constraint changes.

Start practicing smarter

A company-targeted bank is a selection tool, not a magic list. Keep it boring, searchable, and pattern-first. Tag by company and role, but train by pattern and status.

If you want stable prompts to build the bank from, start at the Interview Questions & Answers Hub, supplement with the IQB interview question bank, then run the same loop until follow-ups feel normal: attempt → test → redo → explain → repeat.

References

Frequently Asked Questions

What does a coding interview question bank by company mean?

A searchable set of practice questions tagged to a target company and role, built to support redo-and-explain practice rather than list collecting.

Are company-tagged question lists reliable?

They are useful for narrowing selection, but they are not promises. Treat them as inputs and validate through patterns, follow-ups, and spaced redos.

How big should a targeted set be?

Start small enough to redo and explain. Expand only when your gaps repeat and you need more coverage in specific patterns.

How do I avoid overfitting to one company?

Keep the bank pattern-first. Use company tags to prioritize, then train variants and follow-ups so skills transfer.

How should I use AI with a question bank?

Use it after your attempt for tests, edge cases, constraint changes, and follow-ups, then redo from memory and explain in your own words.