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Explain qualitative vs quantitative user data with engineering examples
Contrast the two modes and give one example per type.
How does UX research integrate into a two-week agile sprint?
This tests if you see research as ongoing discovery, not a predev phase. A good answer covers backlog work across sprints, outcome-based prioritization, and engineer touchpoints in refinement. Red flag: saying research happens only before coding starts.
Generative versus evaluative research: when to use each
Generative precedes design to find needs; evaluative follows a build to test it.
Describe a lightweight research method to validate a feature with no researcher
What it tests: bootstrapping validation without research headcount. Answer outline: unmoderated concept tests or concierge MVPs; behavioral vs attitudinal data; lightweight recruiting. Red flag: shipping A/B tests or using only internal opinions.
How would you instrument a feature to validate qualitative findings quantitatively?
Mapping themes to events, picking guardrail and success metrics, and sampling.
What is PII and how to keep it out of logs
Define PII, redact or mask at the logging boundary, and avoid logging sensitive fields at the source.
Implement verifiable informed consent before a remote usability test
This tests whether you treat GDPR consent as an architectural gate. Strong answers cover CMP-powered pre-session disclosure, gating the test on active consent, server-side recording for auditability, and first-party data alignment.
Explain data minimization and how to apply it to a survey schema
Tests translating GDPR adequacy into schema design. Strong answers: define survey purpose first, map each column to a specific need, exclude PII unless essential, and plan periodic review.
Anonymization vs pseudonymization and key handling
Anonymization is irreversible and exits GDPR scope; pseudonymization is reversible via a separated key; secure that key in a KMS with strict access.
How do you fulfill a GDPR erasure request across data stores?
This tests cross-system deletion under GDPR's 30-day SLA. A strong answer maps PII lineage across S3, databases, and analytics; uses soft deletes for backups; and handles dashboards via reprocessing.
Design a centralized consent platform: core components and database schema
Separate CMF from CMP; build an append-only ledger with versioned wording, per-activity granularity, and real-time revocation.
Apply differential privacy to user behavior queries and explain epsilon trade-offs
Mastery of formal privacy guarantees and noise-based query systems. Inject Laplace or Gaussian noise scaled to query sensitivity; track cumulative epsilon across queries; lower epsilon tightens privacy but increases variance and error bars.
Propose an automated de-identification pipeline for video interview recordings
Propose CV redaction, ASR/NLP for names, human QA, and consent tracking; note model bias and re-ID risk.
What trade-offs matter between moderated usability tests and surveys?
Whether you align research method to product risk and insight type. Great answers contrast surveys for scalable opinions against moderated tests for behavioral observation, weighing fidelity and speed.
Essential UX research plan components for engineering scope
Name parts—problem, goals, method, users, timeline, metrics, risks—and map them to scope, workload, or instrumentation.
How do you validate UX within hard technical constraints?
Tests evaluative research design for constrained, buildable prototypes rather than ideal mocks. Strong answers scope to feasible layers, use Wizard of Oz or stubs, and benchmark against current state. Red flag: testing fantasy UI that ignores API limits.
What technical instrumentation supplements a two-week diary study?
Log anonymized feature frequency, session duration, and device switches; link via participant ID.
Propose two research methods to investigate checkout abandonment
Tests pairing behavioral and attitudinal research. Great answers match funnel analytics to find drop-offs with usability tests to uncover why users cannot complete fields or leave in anger.
What criteria and questions ensure right users for a specialized dev tool?
Designing a recruiting pipeline that qualifies niche experts and secures attendance. A strong answer covers role criteria, layered tech screens, and no-show backups. Red flag: relying on job titles without verifying real tool usage.
Design a one-week lean research plan for a high-risk decision
This tests trading rigor for speed without losing decision signal. A strong answer matches the riskiest assumption to a fast method, sequences generative and validation across five days, and defends bias.