##### 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.