Product Execution PM Interview: Reddit Root Cause Analysis

Welcome to the sixth edition of PM Interview Prep Weekly! I’m Ajitesh, and this week we’re diving deep into root cause analysis—a critical execution skill that shows how you debug product problems in real-time.

The Context

Root cause analysis (RCA) questions are classic Product Execution questions. They test your ability to diagnose problems systematically, work with data, and collaborate cross-functionally to find solutions.

These questions simulate one of the most common situations you’ll face as a PM: Your metrics are down, stakeholders are asking questions, and you need to figure out what happened—fast.Here’s the thing: metrics surprises happen all the time in real product work. Sometimes good, sometimes bad, but always requiring explanation.

Let me share one real-life incide that happened when I was PM at Google to set the context.

When My Data Transfer Metrics Went 3x Overnight

I was managing the data transfer product at Google Cloud. Our metrics has clear pattern and aligned with growth in analytics and migration pipeline.

On one particular instance, I observed that our data transfer volume is up 3x. Not 30%. Three hundred percent.

This was pretty odd! Our monthly business review (MBR as we called it) was the next day. You can’t walk into that room and say “transfer volume tripled, no idea why.” Execs need to know if this is sustainable, if we can handle it, if we should adjust forecasts.

I had less than 24 hours to figure it out.

First, I checked the obvious suspects. Global event? No. Major product launch? No. New enterprise customer? The data showed most volume came from new projects, not existing ones.

Something felt off. These new project IDs had a pattern—they looked like the auto-generated ones you get when individuals (not organizations) create projects.

I emailed our top 10 customers. Nothing unusual on their end.

I spoke to engineering. No recent changes that would explain this. No new features or change in how we count bytes moved.

At this point, I went to the business review with what I had: “We’re seeing unusual growth from new individual projects. Take these numbers with a pinch of salt. I’ll investigate and report back.”

After the meeting, I looped in our finance team. Together, we discovered the truth: crypto miners were using Google Cloud for Chia seed mining at massive scale. They’d found a way to exploit our free tier and scale it up.

We immediately worked with security to identify and block these accounts. Then we implemented a better quota system based on user quality to prevent future abuse.

Crisis managed. But more importantly, a few lessons learned.

My Learning from Debugging Product Issues in Real Life

In this interview question type and in real life RCA product situations, I recommend the three key principles that I generally adhere to:

1. Go Broad, Then Narrow

When you know your product inside out, you have biases. You think you know where the problem is. Resist that temptation.

Start systematic. Check all the usual suspects first—external factors, product changes, customer behavior, technical issues. Only after you’ve eliminated the obvious should you dive into your hunches.

Otherwise, you look like you’re hunting in the dark. Your team needs to see you have a process, not just guesses.

2. Decide Clear Next Steps

Finding the root cause isn’t enough. You need three things:

• Why it happened (the immediate cause)
• The immediate fix (stop the bleeding)
• The long-term fix (prevent it from happening again)

In my case, the immediate fix was blocking the abusive accounts. The long-term fix was implementing quality-based quotas and better monitoring for unusual patterns.

3. Make Post-Mortems Blameless

When metrics go wrong, conduct a post-mortem. But keep it blameless. Read more about this culture of blameless post-mortem.

At Google, we’d ask: “How did we get lucky?” and “What could have gone worse?” These questions help you understand what processes worked and what was missing.

We’re all human. We’ll make mistakes. The goal isn’t to assign blame—it’s to build systems that catch mistakes before they become disasters.

The Interview Application

When you get a root cause analysis question in your PM interview, remember:

1. Show your process: Start broad, systematically eliminate possibilities, then focus
2. Think about data sources: What metrics would you check? Who would you talk to?
3. Consider multiple hypotheses: Don’t lock onto one explanation too early
4. Propose solutions: Both immediate and long-term fixes
5. Think about prevention: How would you catch this earlier next time?

The interviewer isn’t testing whether you can guess the right answer. They’re testing whether you can think systematically under pressure.

The Case

Interviewer: “The number of comments on Reddit is down 5% this week compared to the last one. Why did this happen?”

The Interview Approach

Note: This framework is one approach for systematic root cause analysis. The key is to resist asking endless questions upfront and instead use process of elimination to efficiently identify the cause.

1. Clarify the Problem (2-3 minutes)

Start with minimal, targeted clarifying questions. Resist the temptation to turn this into a Q&A session - you want to show structured thinking, not just ask questions.

• Define the metric precisely (what exactly dropped?)
• Understand the pattern (sudden vs gradual, continuing vs stabilized?)
• Confirm the scope (global or specific segments?)
• Check for context (expected seasonality, recent events?)

That’s it. Stop there. You don’t want your interviewer thinking you’re just fishing for answers.

2. Systematic Elimination (10-15 minutes)

Now lay out your systematic approach using MECE (Mutually Exclusive, Collectively Exhaustive) categories to eliminate possibilities:

• System/Technical Issues: Problems with logging, tracking, metric calculations, or infrastructure failures that could cause false readings
• External Forces: Competitor launches, changes in traffic sources (SEO/referrals), market trends, user behavior shifts, or expected seasonality
• Internal Factors: Product changes (features, UI, algorithms), platform-specific issues, feature cannibalization, policy updates, or performance degradation - this typically requires the deepest investigation
• Uncontrollable Events: Regulatory changes, political actions, natural disasters, or major global events that impact user behavior

Important: Adapt these categories to your specific question. The buckets below work for many metrics problems, but tailor them to sound natural and relevant. For example, for a payment failure issue, you might use “Payment Flow Steps” instead of “Internal Factors.” For a geographic-specific drop, you might add “Regional Factors.” Don’t sound robotic - make it conversational and question-specific.

3. Deep Dive & Next Steps

Once you’ve narrowed to likely causes:

• Drill down: Ask specific data questions to confirm hypothesis
• Validate: Propose how to verify the root cause
• Recommend: Suggest both immediate fixes and long-term solutions

Remember: The interviewer values your systematic approach more than finding the exact answer.

A Real Solution: Systematic Investigation

Here’s how to tackle this case using systematic elimination to uncover root cause.

Step 1: Minimal Clarifications

Start with targeted questions:

• “How are we defining ‘comments’ - all comment activity or specific types?"
• "Was this a sudden drop or gradual decline over the week?"
• "Is this 5% drop global or concentrated in specific regions?”

Let’s say the answers are: All comments, gradual decline over the week (now stabilized), and global phenomenon. Time to investigate.

Step 2: Systematic Elimination (15 minutes)

Then present your MECE categories: “Let me work through four categories systematically to identify the root cause.”

Category 1: System/Technical Issues

Start here because it’s usually quickest to verify or eliminate. I’ll trace the data flow from collection to reporting.

Data Collection Layer (Frontend):

• “Have we changed any tracking scripts or analytics tags?” → No changes
• ”Any issues with third-party analytics tools (Google Analytics, etc.)?” → All functioning normally

Data Processing Layer:

• “Have we changed how we calculate or define ‘unique visitors’?” → No definition changes
• ”Any recent deployments affecting our data pipeline?” → Normal weekly releases, nothing major

Infrastructure Layer:

• “Are we seeing increased error rates (4xx/5xx) or latency issues?” → Page load times normal, no error spikes
• ”Any CDN or database performance issues?” → All systems operational, query times normal

✅ Eliminate this category - Data flow from collection to reporting is intact

Category 2: Internal Factors

This is often where the issue lies. I’ll investigate from recent changes to platform-specific patterns.

Recent Changes (last 2 weeks, working backwards):

• “Any features launched this week?” → Yes, updated comment/upvote button UI on Monday
• ”What was the rollout strategy?” → Started at 10% Monday, now at 60%
• “Does timing/percentage correlate with the drop?” → Not perfectly - 5% drop is steeper than 60% rollout would suggest
• ”Were there A/B test results?” → Yes, showed minimal engagement impact
• ”Any changes the week before?” → No major launches
• ”Algorithm or ranking changes?” → No changes to feed or recommendations

Platform Analysis (broad to specific):

• “What’s the breakdown by platform?” → KEY FINDING: Web down 4%, iOS up 1%, Android up 0.5%
• “So web-specific - let me drill into web. All browsers affected?” → Yes, Chrome, Safari, Firefox similar drops
• ”Is the new UI live on all platforms?” → Yes, including mobile apps (which aren’t affected)
• “Any web-only deployments or experiments?” → No web-specific changes beyond the UI

User Segments & Behavior:

• “Which user cohorts are affected?” → Both new and returning users
• ”Any geographic concentration?” → No, globally distributed
• ”Has user behavior changed (session time, pages per visit)?” → Slight decrease, but not proportional to traffic drop

🔍 Partial finding: Web-specific issue not fully explained by our changes. Need to look externally.

Category 3: External Forces

Since we found web-specific issues, I’ll analyze traffic sources from largest to smallest.

Traffic Source Breakdown (investigating web traffic composition):

• “What are our top web traffic sources and their changes?”
  • Direct traffic (40% of web): Stable
  • Google search (35% of web): Down 15% 🚨
  • Social media (15% of web): Down 2%
  • Other search engines (5% of web): Stable
  • Reddit internal/other (5% of web): Stable

Deep dive on Google search (our biggest drop):

• “When did this decline start?” → Monday morning, accelerated through Wednesday, plateaued Friday
• ”Which types of searches are affected?” → Informational queries most affected (e.g., “best laptop reddit”)
• “Any changes to our search visibility?” → No manual penalties in Search Console
• ”Could this be an algorithm update?” → No major Google updates announced
• This is significant but let me check other factors before concluding

Competitive & Market Context:

• “Are competitors seeing similar patterns?” → Would need competitive intel
• ”Is this seasonal?” → Last summer saw 1-2% dip, not 5%
• “Any industry-wide events?” → Nothing major affecting all social platforms

🔍 Key finding: Google search down 15% is our smoking gun - but why would Google suddenly reduce our visibility?

Category 4: Uncontrollable/External Events

Following the Google search clue - what could reduce our search visibility?

Content Availability (most direct impact on search):

• “Is our content still accessible to Google crawlers?” → Let me check…
• ”Are all subreddits publicly viewable?” → Several large subreddits are now private 🔴
• “Which ones specifically?” → r/technology, r/gaming, r/music - millions of subscribers each
• ”When did they go private?” → Started Monday, more joined throughout the week
• ”Is this coordinated?” → Yes, appears to be organized protest

Understanding the Context:

• “What triggered this?” → Protest against our new API pricing announced last week
• ”How widespread is this?” → Growing - started with few, now dozens of major subreddits
• ”Any regulatory or legal issues?” → No government interventions

Step 3: Deep Dive & Root Cause (8 minutes)

Now I’ll connect the dots between the protest and our traffic drop:

Timeline of Events:

• Day 1: API pricing protest begins → First wave of subreddits go private
• Day 2-3: More subreddits join → Google search traffic starts declining
• Day 4-5: Major subreddits now private → Traffic decline plateaus at -15%
• This timeline matches our observed traffic pattern exactly

Confirming the Root Cause:

Method 1: Traffic Percentage Check

• ~25% of Google traffic historically goes to now-private subreddits
• Google can’t crawl private content (returns 403)
• Google de-indexes inaccessible pages within 24-72 hours
• Math check: 25% content gone × 60% crawled = ~15% search drop ✓

Method 2: Validation Questions

• ”Is direct traffic to private subreddits down?” → Yes, 100% blocked
• ”Are open subreddits maintaining search traffic?” → Yes, stable
• ”Why are mobile apps up?” → Users going directly to Reddit app instead of Google

Root Cause: The moderator protest made major subreddits private → Google lost access to crawl this content → Search traffic dropped 15% → Overall traffic down 5%

Next Steps & Recommendations

Immediate Actions:

• Pull list of top 500 subreddits by search traffic - identify which went private
• Set up war room with eng, community, and data teams - need hourly updates on protest spread
• Monitor escalation: Track if more subreddits join the protest

Short-term Solutions:

• Open dialogue with moderator community
• Offer grace period for pricing adjustments to buy time in dealing with the moderator concerns
• Create temporary landing pages for private subreddits explaining the situation to Google searchers - stop the bleeding

Long-term Considerations:

• Establish our direct channels with top moderators and subreddits so that you are informed about discontent beforehand.
• Evaluate a monetary solution, e.g., revenue sharing or free credits, for certain kinds of developers to set up the change for success.

Practice This Case

Want to try this root cause analysis case yourself with an AI interviewer that challenges your systematic thinking?

Practice here: PM Interview: Root Cause Analysis - Reddit Comments

Further Reading

For a deeper dive into root cause analysis and execution skills:

• More AI Mock Interviews - Find more RCA questions here
• Watch me do this case live Recording of me attemping this question
• Postmortem Culture at Google SRE - Understand how to deal with RCA in real life
• Vibe-Debugging - Not sure how much to take this seriously but I like what Resolve AI is doing in this space

PM Tool of the Week: Sentry

When you’re investigating production issues, having visibility into errors and their root causes is crucial. Sentry is the debugging platform that I find really useful.

Here’s why I recommend it for PMs:

• AI-powered investigations: Their new AI feature provides hypothesis suggestions and can automatically create GitHub tickets with context, turning error logs into actionable insights
• Smart escalation: Automatically escalates critical errors based on your rules, ensuring the right team gets notified when issues require immediate attention
• Integration ecosystem: Works seamlessly with code review tools and CI/CD pipelines - we integrated it with Archie AI to trigger solutions directly from error alerts

Beyond the tool itself, Sentry’s blogs and tutorials are goldmines for PMs wanting to understand error tracking and debugging workflows.

Found a tool that’s transformed your incident response process? Reply and tell me about it!

What’s Next Week?

Next Monday, we’ll tackle another challenging PM execution case. Make sure you’re subscribed to get it in your inbox!

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About PM Interview Prep Weekly

Every Monday, get one complete PM case study with:

• Detailed solution walkthrough from an ex-Google PM perspective
• AI interview partner to practice with
• Insights on what interviewers actually look for
• Real examples from FAANG interviews

No fluff. No outdated advice. Just practical prep that works.

— Ajitesh
CEO & Co-founder, Tough Tongue AI
Ex-Google PM (Gemini)
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