##### Question A social network is building (or refining) a **private account** feature: any user can set their account to private, in which case only approved followers can view their posts. Follow relationships require a request/approve flow — non-followers must send a follow request that the account owner approves or denies, and only approved followers can view posts, stories, reels, and similar content. As a Data Engineer / analyst, work through the following: 1. **Feature understanding & value proposition.** Briefly define the feature and explain the value it creates for users and for the platform, including the key trade-offs. 2. **Entities & dimensions.** List the primary entities/facts and dimensions you would model to analyze this feature — e.g., user type, privacy state, relationship state, follow-request state, viewer role (owner / approved follower / non-follower), content type, session source, platform, and locale. Call out how you would handle privacy state changing over time. 3. **Metrics.** Propose the north-star (core) metrics and guard-rail metrics for the feature. Include the metrics that would most clearly reveal a decline in engagement among **private-account users versus public users** (e.g., DAU, sessions/user, posts/user, outbound follow requests sent, approval rate, impressions, view-through rate, inbound requests, acceptance latency, replies/messages, creator retention). 4. **Investigate an engagement drop.** Engagement for private-account users has dropped week-over-week. Describe your investigation plan: the cuts/slices, funnels, cohorts, and counterfactual/control slices you would examine, and the hypothesis each would confirm or refute. 5. **Experiment to improve the feature.** Recommend one or two experiments or product changes to validate or improve the feature's value proposition. Define the success metrics, guard-rails, and expected trade-offs for each.

This interview question evaluates metric design, causal reasoning, experiment setup, diagnostics, SQL/statistical checks, and recommendations in a realistic interview setting. A strong answer for Analyze private-account product metrics states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

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 Onsite rounds at Meta.

What role is this question designed for?

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

Analyze private-account product metrics

A social network is building (or refining) a private account feature: any user can set their account to private, in which case only approved followers can view their posts. Follow relationships require a request/approve flow — non-followers must send a follow request that the account owner approves or denies, and only approved followers can view posts, stories, reels, and similar content.

As a Data Engineer / analyst, work through the following:

Feature understanding & value proposition. Briefly define the feature and explain the value it creates for users and for the platform, including the key trade-offs.
Entities & dimensions. List the primary entities/facts and dimensions you would model to analyze this feature — e.g., user type, privacy state, relationship state, follow-request state, viewer role (owner / approved follower / non-follower), content type, session source, platform, and locale. Call out how you would handle privacy state changing over time.
Metrics. Propose the north-star (core) metrics and guard-rail metrics for the feature. Include the metrics that would most clearly reveal a decline in engagement among private-account users versus public users (e.g., DAU, sessions/user, posts/user, outbound follow requests sent, approval rate, impressions, view-through rate, inbound requests, acceptance latency, replies/messages, creator retention).
Investigate an engagement drop. Engagement for private-account users has dropped week-over-week. Describe your investigation plan: the cuts/slices, funnels, cohorts, and counterfactual/control slices you would examine, and the hypothesis each would confirm or refute.
Experiment to improve the feature. Recommend one or two experiments or product changes to validate or improve the feature's value proposition. Define the success metrics, guard-rails, and expected trade-offs for each.

Constraints & Assumptions

Preserve the scope, facts, inputs, and requested outputs from the prompt above.
If the prompt leaves a detail unspecified, state a reasonable assumption before relying on it.
Keep the answer interview-ready: concise enough to present, but concrete enough to implement or evaluate.

Clarifying Questions to Ask

Clarify the business objective, unit of analysis, time window, exposure definition, and primary metric.
State assumptions about instrumentation, randomization, sample size, and data quality.
Separate descriptive analysis from causal claims.

What a Strong Answer Covers

A metric framework with primary, guardrail, and diagnostic metrics.
A credible analysis or experiment design with clear assumptions and bias checks.
SQL/statistical logic for segmentation, variance, confidence, and data validation where relevant.
An actionable recommendation that explains trade-offs and next steps.

Follow-up Questions

What sanity checks would you run before trusting the result?
How would you handle novelty effects, seasonality, or selection bias?
What decision would you make if metrics disagree?