ROLE
Frontend
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These are the same questions found in the library, grouped here by the capability each one mainly examines.
1121 questions
Questions 1–50 of 1121 on this page.
A · Professional problem solving and implementation50
- An AI assistant can create tickets, issue refunds, or change accounts on a user's behalf. A refund request times out, and the system cannot tell whether it completed; retrying may issue a second refund. How would you check the outcome, control retries, and ensure actions stay within the user's authorization?
- A document assistant can submit most cases automatically, but mistakes have very different consequences. Human review has reduced errors, but the queue keeps growing. How would you decide which cases to automate or review, and keep the queue within the team’s capacity?
- A generative feature produces good answers, but peak latency and per-request cost exceed the product budget. How would you determine whether the cause is the model, context, retrieval, or call chain?
- After a foundation-model change, the average score improves, but outcomes worsen for a few important customers. How would you decide whether to ship, stage, or roll back?
- Your company wants an internal knowledge assistant, and document permissions change as people move around. How would you make it answer with evidence while keeping it from exposing what a user should not see?
- A resume line saying "used AI to improve engineering efficiency" carries little information. How would you rewrite it into an experience others can probe and verify?
- User satisfaction with a knowledge assistant has fallen, but offline retrieval metrics are unchanged. Which parts of the system would you investigate?
- You ask an AI to review a configuration or generate a piece of code. How do you decide whether it can go live?
- Enterprise knowledge documents may change, be withdrawn, or become restricted, yet users still receive answers citing old content. How would you propagate changes from source documents through chunks, indexes, caches, and existing citations, and prove that an update or deletion took effect?
- Production answers contain incorrect citations or degrade in quality, but prompts and retrieval context may include sensitive data and cannot be logged by default. What telemetry would help distinguish retrieval, model, and tool failures while controlling data exposure?
- A new model raises overall accuracy from 91% to 94%, but errors remain concentrated in cases that affect customer rights. The team wants to ship based on the aggregate score. How would you decide?
- A RAG assistant cites an internal document, but its owner has updated the process and the new version is not indexed yet. How would you define freshness goals, propagation, and answer behavior during the gap?
- A multi-step agent reads project permissions at startup. Before step three, the user is removed from the project, but the task still holds a tool token. How would you design authorization checks and cancellation semantics?
- A user updates account settings and refreshes immediately, but a read replica returns the old value. Reading from the primary adds load, while waiting for replication may time out. How would you design the experience?
- How do you usually handle null values, empty strings, out-of-range access, and invalid input when writing code? Tell me about a time a missing up-front check caused a problem in testing or production, and what habit changed as a result.
- A sign-up form ends up in inconsistent states because of duplicate submissions and weak-network retries. How would you map its states and transitions, and decide which logic to extract into its own unit?
- Implement a small cache that evicts the least recently used entry when full. What time cost does each operation need, and which tests do you write first?
- You need a structure that supports fast insert, fast lookup, and ordered iteration. How would you choose among candidates, and what would you give up?
- When writing code and reviewing it, what checks have become habit for avoiding performance problems such as querying a database inside a loop or holding large objects too long? How do you know they actually work?
- Implement a retry wrapper that retries by error type, supports a maximum attempt count, backoff, and cancellation, and does not overwhelm the dependency. Define the interface before coding, then explain how to make clocks, waits, and tests deterministic.
- After multiple macro and code-generation expansions, compiler errors point only to generated files, leaving users unable to find the original code. How would you design diagnostics?
- Incremental compilation is fast, but changes to a shared header sometimes fail to rebuild all dependent targets. How would you find the dependency-tracking gap?
- A cache can make reads faster and can also show users stale content. How do you decide whether a piece of data suits caching, and how do you notice when it goes wrong?
- Two jobs run fine alone but occasionally hang when run together. What conditions must hold for a deadlock, and how would you confirm that is the cause?
- After a page sits idle, the first request is much slower than later ones. What network-level causes could explain it, and how would you tell them apart?
- A long-running process keeps using more memory, while object counts in heap snapshots fluctuate. How would you use evidence to distinguish a true leak, intentional cache retention, fragmentation, or growth in native memory?
- The first screen of an image-heavy page opens slowly. How would you work backwards from the symptom to the cause, and what would each improvement cost?
- What happens between writing a line of code and the CPU running it? Pick a language you know and walk through the main stages.
- After reads move to replicas, users sometimes cannot see a change immediately after a successful write. How would you define and handle stale reads?
- Your endpoint works in local tests, but during integration a frontend colleague insists it errors or returns nothing. What means do you use to locate the problem, and how do you produce evidence the other person will accept?
- In production the same order is occasionally submitted twice, and you cannot reproduce it locally. How would you narrow it down, and would you stop the bleeding first or add observability first?
- After splitting a large frontend bundle into several smaller ones, the first screen got slower. What causes would you suspect, and how would you verify them?
- A cache key is invalidated and logs show a database reread, yet some users still see old data. Switching to the primary helps but does not eliminate it. How would you investigate caches, replicas, and clients?
- A request calls four downstream services, each using a full two-second timeout. End-to-end tail latency is high, and clients continue work after the response is no longer useful. How would you propagate deadlines and budgets?
- On-call engineers receive many alerts each day, yet user complaints and service objective violations still go unnoticed. How would you redesign alerting?
- An old system keeps accumulating new business and is becoming hard to maintain. How would you create an adapter boundary between old and new systems so legacy rules and data models do not spread into new code? Explain the migration steps and any compromises or risks you would accept.
- Tell me about an architecture upgrade. Why did it have to be upgraded rather than patched further, what challenges came up during migration, how did you keep the business running, and did the benefit materialize?
- For a given capability, how do you decide between building it, adopting open source, or buying it? How do building and open source differ in cost, control, maintenance, and risk, and when should you switch from one to the other?
- A repository accepts external contributions, and CI runs build scripts, reuses caches, reads test credentials, and produces release artifacts. How would you separate untrusted execution, credential permissions, and cache boundaries so code cannot read secrets or poison trusted artifacts?
- Cloud costs keep rising and finance asks for cuts while teams are improving reliability. How would you find savings without shifting risk elsewhere?
- How do you identify and record technical debt and pay it down without stopping business delivery? Which debts are worth paying and which can be left for good, and how do you judge?
- Your solution depends on a lower-level service or library. If it changes substantially, how would you migrate, fall back to a version that preserves core features, or replace it?
- The root cause of an incident is still unknown, but business stakeholders ask when service will recover. How would you update them, communicate uncertainty, and avoid misleading commitments?
- In the technical area you work in, how is the industry developing? Is your solution adapting to that trend or going against it?
- After a release goes wrong, the team says “we can roll back.” How would you check that this is true and not just a line in the plan?
- A company’s frontend projects live in separate repositories, and someone proposes merging them into a monorepo. How would you decide whether to do it, and how would you validate it in steps?
- What technical gains did this project bring? How do you measure them, and what complexity and maintenance cost did you pay for them?
- You take over a module that is about to ship and has very low test coverage, and time is short. How do you decide which tests to add first, and how do you explain that order to the team?
- The framework allows concurrent background UI updates, but a slow response occasionally overwrites a newer selection. How would you design concurrent state updates?
- A complex form has field validation, cross-field rules, and server errors that overwrite one another during edits. How would you divide framework state from business state?
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