ROLE
Data engineering
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These are the same questions found in the library, grouped here by the capability each one mainly examines.
453 questions
Questions 101–150 of 453 on this page.
A · Professional problem solving and implementation19
- A build registry must clean up years of images, but production services may still roll back to old versions. How would you define retention and artifact recoverability?
- An admin console must retain operation records that cannot be casually altered, support queries by actor and object, and handle years of growth. How would you design writes, indexes, and retention?
- Consumers are slower than producers, so the message queue keeps growing while upstream requests still succeed. Where would you apply backpressure, and how would you protect recovery capacity?
- API latency rises while database CPU remains low, and application logs show connection waits. How would you distinguish connection leaks, an undersized pool, and slow transactions holding connections?
- After writing an order to a database, a service must publish an event downstream; the process may crash at either step. How would you design durable publication and handle duplicates and ordering?
- In an admin console, inserts and deletes while paging by creation time cause duplicate or missing rows. How would you compare offset and cursor pagination and define the consistency expectation?
- An API runs across multiple instances, and a local request limiter makes the total allowance grow with each instance. How would you design request-rate controls for bursts, accuracy, and failures, and where would you keep their state?
- A storage service shifts from read-heavy to sustained writes, and the team is comparing B-tree and LSM approaches. Which workload characteristics would guide the choice, and what read/write amplification costs would you examine?
- An organization must move several repositories from an old CI system to a new platform without pausing delivery across teams. How would you sequence migration and decide which repositories enter each wave?
- A service increases its worker threads from 16 to 256, but throughput does not improve and CPU use rises. How could context switching contribute, and what evidence would you use to verify it?
- After a database field change, only some downstream jobs fail intermittently. How would you identify consumers still using the old contract and decide between rollback and a compatibility fix?
- A reconciliation shows timestamps from two data centers placing event A before B, while the message trace shows B triggered A. How would you distinguish clock skew, processing delay, and business causality, and choose trustworthy diagnostic evidence?
- Monthly aggregates initially match detail reports, but the gap grows over several weeks. How would you narrow the cause among definition changes, late data, duplicate processing, and historical backfills?
- Message backlog often falls after scaling out, then returns a few hours later. What producer, consumer, and message characteristics would you inspect to locate the actual bottleneck?
- Average service latency is stable, but p99 has risen for a week and complaints cluster among a few large customers. How would you locate the problem and choose what to verify first?
- Design a service that lets several product teams submit tasks to run after a specified time; tasks may be canceled, rescheduled, or submitted more than once. How would you divide the data model and execution components?
- Hundreds of repositories pin different third-party versions, and security teams struggle to identify exposure when a severe vulnerability appears. How would you establish dependency updates and risk handling?
- A defect appears only in production and cannot be reproduced in development or staging. How would you determine whether the cause is environment, traffic shape, or data characteristics, and improve environment design?
- Several services read feature flags and audience rules on request paths. How would you design the control plane and local evaluation so behavior remains predictable when the control service fails?
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B · Business understanding and domain modeling31
- A partner prepays credits that expire, and a customer uses or refunds them in several batches around the expiry date. How would you represent credits, consumption, expiry, and refunds in settlement?
- In a system that handles money, how should accounting and billing be designed so the numbers are clear and reconcile? How do you model accounts, entries, holds, settlement, and reconciliation, and how do you trace and correct an error?
- How should amounts, currencies, rounding, and time zones be expressed in a transaction and settlement model? Which practices look fine but break at reconciliation?
- An order may use a membership discount, channel offer, and coupon, with exceptions by product, contract, or region. How would you define eligibility and calculation order so customers see a consistent amount and finance can reconcile refunds?
- The revenue share between a platform and its creators changes in tiers with monthly volume. How would you express this settlement rule so product, engineering, and finance can each verify it independently?
- The business says data must be real time, but no one can explain the cost of a fifteen-minute delay. How would you decide the architecture and freshness target?
- Product, finance, and growth teams each report “active users,” but the numbers differ. What would you ask before deciding whether to standardize the definition?
- Taking concepts such as orders, contracts, and settlements, how are they mapped into the data model? Which concepts were split into several entities, which were merged, and why?
- A user consented to product analytics, then withdrew consent. Derived metrics already reached reports, experiments, and model-training data. How would you model consent state, purpose scope, and propagation?
- A producer plans to rename a field and change an enum’s meaning, while a dozen teams depend on the current schema. How would you manage the change?
- A business object moves across teams as owners change. Audit must reconstruct the owner on a past date, while daily reporting needs current ownership. How would you model both queries?
- A user asks to have personal data deleted, while finance must retain transaction records for audit. How would you define the boundary among deletion, anonymization, and traceability?
- In a behavioral event table, what exactly does one row represent? Explain how you settle its grain, and which statistics go wrong when the grain is unclear.
- A monthly close report was sent to leadership. Days later, a late refund and price correction arrive. The business wants current figures updated, while finance must explain the prior report. How would you model this?
- How does your own business model interact with the industry's or a partner's common model? When fields, definitions, or concepts do not line up, how do you handle it?
- Mobile events may arrive days late, while reports must reflect event time. How would you model and process late, corrected, and duplicate events?
- A critical dataset is reused by several products and reports, but no one can explain its sources, owners, or sensitive fields. How would you improve governance without blocking current use?
- An account moves from sales to customer success mid-month. Monthly metrics must show the outcome and who owned it at each point. How would you define ownership events and reporting rules?
- A fee rule changes today, but a disputed order was created under the old rule. How would you preserve rule versions and transaction facts so finance can reproduce the original calculation?
- A company buys 100 seats used across subsidiaries. Headquarters signs the contract, but some subsidiaries want separate invoices and permission administrators. How would you distinguish customer, contract, organization, account, member, and seat so access and billing stay consistent?
- Customer master data has separate identifiers and states in CRM, billing, and product systems, so downstream reports disagree. How would you establish explainable sources of truth?
- A quality check occasionally produces false alarms and blocks downstream jobs; removing it once let bad data reach settlement. How would you design tiered handling?
- A model feature will be retired, but several online models and historical reports still depend on it. How would you plan retirement and replacement?
- An online feature must refresh every second, but it affects few requests and its maintenance cost keeps rising. How would you decide whether to keep it real time?
- A risk model uses later default as its training label, but many transactions have no final outcome before the observation window ends. How would you handle label maturity, sample inclusion, and retrospective evaluation?
- You are building a 'days active in the last 7 days' feature for a model that predicts next-day churn. How do you define its time semantics so training and online prediction mean the same thing?
- An order has a single status field that means both fulfillment progress and payment result, and several teams read it. How would you split it into two concepts and move old data and old consumers over smoothly?
- A service changes eligibility from “current account status” to “account status at application time plus a risk-review result.” Support needs to explain historical denials, and audit must reproduce the original decision. How would you evolve rules and data models without conflating old and new outcomes?
- Duplicate registration created two customer entities later confirmed to be the same company. Reports should unify them, while contracts, approvals, permissions, and personal actions must retain original responsibility. How would you model the merge?
- A business rule is corrected retroactively. Reports need corrected results, while decision-makers also need the results available at the time. How would you manage both historical views?
- A producer adds an order status, and an old consumer rejects the whole message because enum validation is strict. Teams disagree over who must update, and consumers have different owners. How would you design compatibility and rollout order?
No questions in this section match your target. ·