How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a hard difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at Meta.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at Meta during technical interviews.

Determine User Need for In-App Video Call Feature

Last updated: Mar 29, 2026

Quick Overview

This question evaluates a data scientist's competencies in product analytics, experimentation design, causal inference, metric definition and interpretation, segmentation, and assessing engineering trade-offs within the Analytics & Experimentation domain.

Scenario

A consumer messaging app is considering launching an in-app Video Call feature. You have access to full historical user and call data (e.g., messaging, audio call usage, device/network attributes, and user engagement/retention).

Questions

Using the available data, how would you determine whether users need an in-app Video Call feature before launch?
After launch, what success metrics and guard-rail metrics would you track to evaluate feature quality?
Describe step-by-step how you would design and run an A/B test for this feature.
What are the engineering pros and cons of enforcing a hard hang-off (automatic call termination) policy, and how might that affect experiment results?

Hint: Think funnel, engagement, retention, power-user segmentation, experiment unit, sample size, run duration, and potential user experience trade-offs.

Solution

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