Research and Product Analytics Tools Questions

Proficiency with user experience research and product analytics platforms used to generate qualitative and quantitative insights. This includes hands on use of remote and moderated research tools such as UserTesting Maze and Optimal Workshop and prototyping or usability tools such as Figma; and analytics platforms such as Google Analytics Amplitude and Mixpanel for event tracking funnel analysis cohort analysis retention analysis and experiment evaluation. Candidates should be able to design studies recruit participants select metrics instrument events interpret mixed methods results create dashboards and translate findings into product improvements. Include knowledge of experiment design A B testing basics data quality and how to combine qualitative observations with quantitative signals.

MediumTechnical

21 practiced

Outline a QA plan to validate an analytics implementation that uses Google Tag Manager (GTM) to fire events to Google Analytics and Amplitude. Include manual tests, automated smoke tests, staging checks, expected payload validation, event deduplication checks, and criteria you would use to approve the implementation for production.

EasyTechnical

30 practiced

You're launching a 'saved-items' feature. List six important quantitative and qualitative metrics to track during the first 30 days (for example: adoption rate, saved-to-purchase conversion, average saved per user, user feedback themes). For each metric specify how you'd measure it, what events or survey questions to instrument, and why it matters.

EasyTechnical

22 practiced

What is instrumentation QA in the context of product analytics? Provide a concrete checklist (at least six checks) you would run to validate event instrumentation for a new feature across dev, staging and production environments. Include both manual checks and automated validation ideas.

MediumTechnical

22 practiced

Describe how you would instrument events and compute 1-day, 7-day and 30-day retention for a content-consumption feature. Specify which events to track, how to define the cohort (first_consumption_date or signup), user identification approach, how to handle multiple events per day, and outline the SQL logic or analytics query steps you'd use to produce a retention table.

HardTechnical

22 practiced

Enumerate and explain statistical pitfalls commonly encountered in A/B testing such as p-hacking, peeking at results, multiple comparisons, non-independence of observations, and novelty effects. For each pitfall describe concrete policies, tooling, or analysis practices you would deploy to minimize false positives and ensure decisions are robust.

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