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Meta·Software Engineer·Onsite - Product Sense / Strategy·Senior

SeniorPrefer not to say
May 2026

Summary

Meta PM interview, one scenario-based question about a metric drop in the ad creation flow. Pretty focused session, no fluff.

Questions Asked (1)

Q1

As the PM for Facebook Ad Manager, you notice the ad publishing rate dropped 10% starting around 10PM last night. Walk through what you'd do.

Product Analytics & MetricsRoot Cause AnalysisCross-functional Alignment
Author's notes

This is a classic metric drop question but the ad creation workflow angle makes it a bit more layered than your average DAU dip scenario.

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AI HintsAI Generated

Suggested Approach

Start by clarifying the scope and impact of the drop, then systematically check for internal and external causes, prioritizing recent changes and data anomalies. Communicate findings and next steps to stakeholders throughout the investigation.

Pro tip: Always validate the data first—drops can be due to logging or tracking issues, not actual product problems. Also, consider time-based factors like scheduled jobs or regional outages that align with 10PM.

1. Clarify and Scope

Ask clarifying questions to understand the metric definition, time zone, and whether the drop is global or segmented. Confirm the exact drop percentage and baseline.

2. Validate Data

Check if the drop is real by verifying data pipelines, logging, and dashboards. Rule out tracking errors, delayed data, or reporting bugs.

3. Identify Correlates

Segment the data by dimensions like region, platform, ad type, and user cohort to localize the issue. Look for patterns that align with the 10PM start time.

4. Hypothesize and Test

Generate potential causes (e.g., code deploy, external outage, policy change) and test them using logs, deployment history, and system health metrics.

5. Communicate and Resolve

Update stakeholders with findings and impact, and if a cause is found, implement a fix or rollback. Document learnings for future incidents.

Key Points to Mention

  • Check recent code deployments or configuration changes around 10PM.
  • Consider external factors like Facebook API changes, competitor actions, or network issues.
  • Analyze user behavior: Are users seeing errors? Is there a drop in ad creation attempts or successful publishes?
  • Involve cross-functional teams (e.g., infrastructure, data science) early if needed.
  • Set up alerts and monitoring to prevent future similar drops.
  • Prioritize based on business impact: revenue loss, advertiser trust, etc.

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.