ROLE
Data engineering
Compare this role’s stage expectations →
These are the same questions found in the library, grouped here by the capability each one mainly examines.
453 questions
Questions 1–50 of 453 on this page.
A · Professional problem solving and implementation50
- Two reviewers often disagree about the same set of AI outputs. How would you tell whether the issue is annotator disagreement, an unclear evaluation standard, or model instability?
- You built a customer-support Q&A prototype. How would you design an evaluation set to decide whether it may only assist retrieval, or can reply to customers directly?
- What does an algorithm model and the features it depends on truly cost? How do you count training, inference, feature computation and storage, labeling, and maintenance, and how do you judge whether a feature is worth keeping?
- 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- Enterprise knowledge documents may change, be withdrawn, or become restricted, yet users still receive answers citing old content.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?
- 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?
- A very large CSV must be validated without loading the whole file into memory. On malformed rows, the tool should report line numbers and continue checking. How would you design the parser interface?
- A query filters on an indexed column and is still slow. Explain how an index lookup works and what can keep it from helping.
- 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?
- Your service handles requests with threads and a colleague proposes processes instead. What would you compare, and when would you push back?
- What happens between writing a line of code and the CPU running it? Pick a language you know and walk through the main stages.
- When two transactions modify the same data at once, what anomalies can occur? What does each isolation level prevent, and what does it cost?
- A new field must be backfilled across hundreds of millions of rows. The first run increases lock waits and replication lag. How would you decide whether to pause, batch, throttle, or use an online migration?
- A new index speeds up queries, but write latency and storage growth worsen noticeably. How would you decide whether to keep it?
- After reads move to replicas, users sometimes cannot see a change immediately after a successful write. How would you define and handle stale reads?
- After cache expiry, many requests hit the origin at once, and recovery causes a second traffic spike. How would you diagnose and suppress a cache stampede and warm-up storm?
- An API has been slow since yesterday, and there is no release on record. Given logs, monitoring, and database slow-query records, in what order would you investigate?
- 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?
- Producer logs show an event published and the broker acknowledged it. The consumer has no visible error, but the business object did not change. How would you distinguish acknowledgment semantics, dead letters, filters, deserialization, and duplicate handling?
- 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?
- Overall capacity is sufficient after sharding, but a few tenants still overload hot keys. How would you reassess the partition strategy?
- 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?
- A long-lived credential is shared by several services. Security requires prompt rotation, but all dependents cannot be stopped at once. How would you proceed?
- 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?
- Some production resources differ from their declared configuration, including undocumented emergency fixes. How would you distinguish intentional changes from unexplained differences, assess the risk of overwriting state, restore trust, and prevent recurrence?
- A release must reach thousands of runtime nodes with varying versions and network conditions. How would you design rollout batches, health checks, and pause criteria?
- 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?
- The application can be rolled back, but a database migration removed an old field and the previous version can no longer start. How would you redesign release order and rollback?
- 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?
- Two services write directly to the same business table, and an immediate shutdown is impossible. How would you gradually establish a single data owner?
No questions in this section match your target. ·