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 medium 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 Engineer candidates at Meta during technical interviews.

Define success metrics for a social feed

Quick Overview

This question evaluates a candidate's competency in metric design, analytics instrumentation, and experimentation for product features, testing their ability to define measurable primary and guardrail metrics from event-level data in the Analytics & Experimentation domain.

You are evaluating a change to the main social feed in a large-scale consumer app. Assume events are logged to a single analytics table with impression-level and action-level events.

Minimal events table (to ground definitions):

user_id, session_id, event_time (UTC), tz_offset_min
event_type ∈ {feed_load, impression, like, comment, share, save, hide, report, follow, unfollow, crash}
impression_id, content_id, creator_id
dwell_ms (on impression), server_latency_ms (on feed_load)
device_integrity ∈ {PASS, FAIL}, bot_score ∈ [0,1]

Specify:

(a) The primary success metric(s) with exact formulas (numerator/denominator and units).

(b) Guardrail metrics (e.g., latency, content quality, reliability) with formulas and units.

(c) The eligible population and unit of analysis (e.g., user-day, session, impression) for the primary metric and guardrails.

(d) Data hygiene rules: how to handle bots, empty sessions, outliers, and time zones.

(e) How each metric would be computed from the events table (outline the steps and any key joins/windowing).

Finally, discuss the trade-offs between engagement, quality, and reliability, and how your metric set balances these.

Quick Overview

Define Success Metrics for a Social Feed Feature

You are evaluating a change to the main social feed in a large-scale consumer app. Assume events are logged to a single analytics table with impression-level and action-level events.

Minimal events table (to ground definitions):

user_id, session_id, event_time (UTC), tz_offset_min

event_type ∈ {feed_load, impression, like, comment, share, save, hide, report, follow, unfollow, crash}

impression_id, content_id, creator_id

dwell_ms (on impression), server_latency_ms (on feed_load)

device_integrity ∈ {PASS, FAIL}, bot_score ∈ [0,1]

Specify:

(a) The primary success metric(s) with exact formulas (numerator/denominator and units).

(b) Guardrail metrics (e.g., latency, content quality, reliability) with formulas and units.

(c) The eligible population and unit of analysis (e.g., user-day, session, impression) for the primary metric and guardrails.

(d) Data hygiene rules: how to handle bots, empty sessions, outliers, and time zones.

(e) How each metric would be computed from the events table (outline the steps and any key joins/windowing).

Finally, discuss the trade-offs between engagement, quality, and reliability, and how your metric set balances these.

Define success metrics for a social feed

Quick Overview

Solution

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Define success metrics for a social feed

Quick Overview

Solution

Comments (0)