7 System Design Interview Mistakes That Cost You the Job

"We're going to pass on this candidate."

I've written that line on interview feedback hundreds of times. As someone who's conducted over 200 system design interviews at FAANG companies, I've seen brilliant engineers fail, not because they lacked technical knowledge, but because they made avoidable mistakes.

The frustrating part? Most candidates make the same mistakes. And they're completely fixable.

In this guide, I'll share the seven most common system design interview mistakes I see, with real examples and specific fixes for each. If you're preparing for system design interviews, this might be the most valuable thing you read.

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The Real Reason Candidates Fail

Before we dive into specific mistakes, understand this: system design interviews don't test knowledge. They test judgment.

Interviewers already know you can Google "how to build Twitter." What they're evaluating is:

• Can you break down ambiguous problems?
• Do you ask the right questions?
• Can you make reasonable trade-offs?
• Do you communicate technical ideas clearly?
• Would you be effective leading a real design discussion?

Every mistake in this guide maps back to one of these evaluation criteria. Understanding that helps you see why each mistake is so costly.

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Mistake #1: Jumping Into Design Without Clarifying Requirements

What it looks like:

Interviewer: "Design a notification system."

Candidate: "OK, so we'll have a load balancer, then API servers, then we'll use Kafka for the message queue, and..."

The candidate starts drawing boxes and arrows before understanding the problem.

Why This Kills Your Interview

When you skip requirements, you signal three things to the interviewer:

1. You don't think before acting , In real engineering, this leads to wasted weeks building the wrong thing.

2. You might build systems nobody needs , Without understanding requirements, how do you know what to optimize?

3. You're not a good technical partner , Senior engineers clarify before committing.

The Fix

Spend the first 5 minutes asking clarifying questions. Here's a framework:

Functional requirements:

• "What are the core features we need to support?"
• "Who are the users? What actions can they take?"
• "Are there any features we should explicitly exclude?"

Non-functional requirements:

• "What's the expected scale? Users, requests per second?"
• "What are the latency requirements?"
• "Is this read-heavy or write-heavy?"
• "Global or single region?"
• "What's more important: consistency or availability?"

Real Example

Bad approach:

"Design a URL shortener. Let me start by explaining the architecture..."

Good approach:

"Before I start designing, I'd like to clarify a few things. For a URL shortener:

First, what's the scale? Should I design for millions of URLs or billions?

Second, what features do we need? Just creating and redirecting short URLs, or also analytics, custom aliases, expiration?

Third, what are the latency requirements? Is sub-100ms redirect critical?

Fourth, should short URLs be unique globally, or can we have duplicates?"

The second approach shows you think before coding. That's exactly what interviewers want.

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Mistake #2: Skipping Back-of-Envelope Math

What it looks like:

Candidate: "We'll store all the data in PostgreSQL."

Interviewer: "How much data are we talking about?"

Candidate: "Um... a lot? We'll probably need to shard eventually."

The candidate has no idea whether they need one server or a hundred.

Why This Kills Your Interview

Scale estimation separates senior engineers from junior ones. Without understanding the numbers, you can't make informed design decisions.

The interviewer thinks:

• "They don't know if this design actually works."
• "They can't tell if their solution is over-engineered or under-engineered."
• "They'll propose the wrong architecture for the wrong scale."

The Fix

Spend 3-5 minutes on quick math. You don't need exact numbers, order of magnitude is enough.

Key calculations:

Quick reference numbers:

Real Example

Bad approach:

"We'll use Redis for caching to speed up reads."

Good approach:

"Let me estimate the scale. With 100 million DAU and 10 reads per user per day:

• 1 billion reads/day = ~12,000 reads/second
• At peak (3x average), that's ~36,000 reads/second

A single PostgreSQL server handles maybe 10,000 simple queries/second. So we need either read replicas or caching. Since the data is highly cacheable, URLs don't change, I'd add Redis caching. This drops database load by 90%+."

The math justifies the design decision. That's what interviewers want to see.

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Mistake #3: Over-Engineering the Solution

What it looks like:

Candidate: "We'll use Kubernetes for orchestration, Kafka for messaging, Elasticsearch for search, Redis for caching, PostgreSQL for the primary store, Cassandra for write-heavy data, and a GraphQL API with a service mesh..."

For a simple feature that handles 1,000 users.

Why This Kills Your Interview

Over-engineering shows poor judgment. It suggests you:

1. Can't match solutions to problems , You're using enterprise patterns for startup problems.

2. Will slow down your team , Complex systems take longer to build and maintain.

3. Might be compensating , Sometimes candidates add complexity to seem impressive. It backfires.

The Fix

Start simple and add complexity only when needed. Ask yourself:

Before adding a component:

• "What problem does this solve?"
• "Can I solve this more simply?"
• "Does the scale justify this complexity?"

Red flags you're over-engineering:

• More than 5-6 components in your initial design
• Using technologies you can't explain deeply
• Adding features the interviewer didn't ask for
• Sharding before you've hit single-server limits

Real Example

Bad approach (for a startup with 10K users):

"We'll need a microservices architecture with separate services for users, posts, and notifications, each with their own database, communicating via Kafka, deployed on Kubernetes with a service mesh for observability..."

Good approach:

"At 10K users, we don't need distributed systems yet. I'd start with a monolithic application, single PostgreSQL database, basic Redis cache for sessions, deployed on a single cloud instance.

This handles our scale easily and lets us iterate quickly. When we hit scale limits, probably around 100K users, we can revisit. Would you like me to discuss what changes we'd make at higher scale?"

The second approach shows judgment. You know what's appropriate for the scale.

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Mistake #4: Failing to Discuss Trade-offs

What it looks like:

Candidate: "We'll use Cassandra for the database."

Interviewer: "Why Cassandra?"

Candidate: "It's good for scale."

No discussion of what you gain or give up with that choice.

Why This Kills Your Interview

Engineering is about trade-offs. Every decision has pros and cons. When you don't discuss them:

1. You seem like you're reciting, not thinking , Did you actually evaluate options?

2. You miss the opportunity to show depth , Trade-off discussions are where you demonstrate expertise.

3. You make the interviewer do the work , They have to probe to understand your reasoning.

The Fix

For every significant decision, proactively discuss:

1. What alternatives you considered
2. Why you chose this option
3. What you're giving up

Use this format: "I'm choosing X over Y because [reason]. The trade-off is [what we lose], which we accept because [why it's acceptable]."

Real Example

Bad approach:

"We'll use NoSQL for the database because it scales better."

Good approach:

"For the database, I'm considering PostgreSQL and Cassandra.

PostgreSQL gives us ACID transactions and complex queries, but it's harder to scale horizontally.

Cassandra gives us easy horizontal scaling and high write throughput, but we lose complex queries and strong consistency.

For this use case, a messaging app with high write volume and simple access patterns (mostly key lookups), I'd choose Cassandra. We don't need complex joins, and eventual consistency is fine for message delivery.

The trade-off is that we'll need a separate system if we ever need analytical queries across messages."

This response demonstrates:

• You know both options
• You understand the trade-offs
• You can match solutions to requirements
• You acknowledge limitations

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Mistake #5: Not Driving the Conversation

What it looks like:

The candidate waits for the interviewer to ask questions. Long silences. The interviewer has to pull information out.

Interviewer: "What about caching?"

Candidate: "Oh yes, we can add Redis."

Interviewer: "What would you cache?"

Candidate: "User profiles?"

Interviewer: "What's the invalidation strategy?"

Candidate: "TTL?"

The candidate is reactive, not proactive.

Why This Kills Your Interview

System design interviews simulate technical leadership discussions. If you can't drive a design conversation:

1. You won't lead technical discussions on the job , Senior engineers shape conversations, they don't just respond.

2. The interviewer has to work too hard , They want to evaluate you, not carry you.

3. You seem less senior , Junior engineers wait for direction. Senior engineers provide it.

The Fix

Think out loud. Share your reasoning as you design.

Structure your presentation. "I'm going to cover three areas: the core flow, scaling considerations, and failure handling."

Check in, don't wait. "I've covered the basic architecture. Should I go deeper on the database design, or would you like to discuss something else?"

Acknowledge gaps. "One area I want to address is how we handle the case where..."

Real Example

Bad approach (passive):

Candidate draws diagram silently

Interviewer: "What's this component?"

Candidate: "A load balancer."

Interviewer: "Why do you need it?"

Good approach (active):

"Let me walk you through the architecture.

Starting with the client, requests hit our load balancer first. I'm including this because we need to distribute traffic across multiple API servers for both scalability and fault tolerance. We'd use something like AWS ALB.

The load balancer routes to our API servers. These are stateless, so we can scale horizontally. I'm thinking 5-10 servers based on our QPS estimate earlier.

Next, before we hit the database... actually, let me pause here. Do you want me to continue with the data layer, or dive deeper into any component?"

The second approach shows leadership. You're guiding the conversation.

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Mistake #6: Giving Shallow Deep Dive Answers

What it looks like:

Interviewer: "How does the consistent hashing work?"

Candidate: "It distributes data across nodes evenly."

That's a Wikipedia summary, not an answer.

Why This Kills Your Interview

The deep dive is where strong candidates shine. When you give shallow answers:

1. You seem like you've memorized, not understood , Anyone can memorize definitions.

2. You miss the chance to differentiate , Most candidates fail here. If you succeed, you stand out.

3. The interviewer doubts your stated experience , If you've used these systems, you should know more.

The Fix

When asked to go deep, use this structure:

1. Start with the concept (1 sentence)
2. Explain how it works (details, steps)
3. Give a concrete example
4. Discuss edge cases or trade-offs

Real Example

Bad approach:

"Consistent hashing distributes data evenly across servers and makes it easy to add or remove nodes."

Good approach:

"Consistent hashing solves the problem of data redistribution when nodes change.

Here's how it works: imagine a ring of values from 0 to 2^32. We hash each server to a position on this ring. To find which server owns a key, we hash the key and walk clockwise until we hit a server.

Why is this useful? In normal hashing, if you have 4 servers and add a 5th, most keys change servers, hash(key) % 4 vs % 5 gives different results. With consistent hashing, adding a server only moves keys between two adjacent servers on the ring.

One edge case: with few servers, they might cluster on the ring, creating uneven distribution. The fix is virtual nodes, each physical server gets multiple positions on the ring. This spreads data more evenly.

Systems like DynamoDB and Cassandra use this. Cassandra uses 256 virtual nodes per physical node by default."

This answer demonstrates:

• You understand the problem it solves
• You can explain the mechanism
• You know implementation details
• You've thought about edge cases
• You know real-world applications

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Mistake #7: Ignoring Failure Scenarios

What it looks like:

The candidate designs a system where nothing ever fails. No discussion of:

• What happens if the database goes down
• How to handle network partitions
• What if the cache is unavailable
• How to recover from failures

Why This Kills Your Interview

Real systems fail constantly. If you don't address failure scenarios:

1. Your design isn't production-ready , Systems without failure handling don't survive.

2. You seem inexperienced , Anyone who's operated systems knows failure is normal.

3. You're missing a major evaluation criteria , Interviewers specifically look for this.

The Fix

For each major component, proactively discuss:

1. What happens if it fails?
2. How do we detect the failure?
3. How do we recover?
4. What's the user experience during failure?

Common failure patterns to address:

Real Example

Bad approach:

"Users write data to the database, and the write is complete."

Good approach:

"Let me discuss what happens when things fail.

If our primary database goes down, we have a standby replica that can be promoted. This takes about 30 seconds. During failover, writes fail but reads continue from read replicas. The user sees an error if they try to submit during those 30 seconds.

If Redis cache goes down, we fall back to the database. Latency increases from 5ms to 50ms, but the system stays functional. We'd set up alerts on cache hit rate drops.

For the message queue, if Kafka is unavailable, we write to a local buffer and retry. Messages aren't lost, just delayed. If the buffer fills up, we return errors to the client with retry-after headers.

We'd use circuit breakers between services so one failing service doesn't cascade to others."

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How These Mistakes Map to Interview Feedback

Here's what interviewers actually write when candidates make these mistakes:

See the pattern? These are all about judgment and communication, not knowledge.

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The Mistake-Free Framework

Here's how to structure your interview to avoid all seven mistakes:

Minutes 0-5: Requirements (Avoid Mistake #1)

• Ask functional requirements questions
• Ask non-functional requirements questions
• Summarize what you heard

Minutes 5-10: Estimation (Avoid Mistake #2)

• Calculate read/write QPS
• Estimate storage needs
• Identify bottlenecks

Minutes 10-25: Architecture (Avoid Mistakes #3, #4, #5)

• Start simple, add complexity as needed
• Explain each component as you add it
• Discuss alternatives and trade-offs
• Drive the conversation, check in with interviewer

Minutes 25-40: Deep Dive (Avoid Mistakes #6, #7)

• Go deep on 2-3 components
• Discuss failure scenarios proactively
• Answer follow-up questions with detail

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Quick Self-Assessment

Before your next interview, practice with these questions:

After you finish designing, ask yourself:

1. Did I clarify requirements before designing?
2. Did I do back-of-envelope math?
3. Is my design appropriate for the scale?
4. Did I discuss trade-offs for major decisions?
5. Did I drive the conversation or wait for prompts?
6. Can I explain each component in depth?
7. Did I address failure scenarios?

If you can answer "yes" to all seven, you're ready.

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Summary: The Path to Success

The candidates who pass system design interviews aren't necessarily smarter or more experienced. They're the ones who avoid these common mistakes and demonstrate judgment through clear communication.

Fix these seven mistakes, and you'll pass interviews that previously felt impossible.---

Frequently Asked Questions

What if I don't know the answer to a deep dive question?

Be honest: "I'm not deeply familiar with that specific aspect, but based on first principles, I would expect..." Then reason through it. Interviewers respect honesty and problem-solving more than faked expertise.

How do I know if I'm over-engineering or under-engineering?

Match your solution to the scale. For 1,000 users, a single server is fine. For 100 million users, you need distributed systems. If you're unsure, ask: "Does this scale require [X]?" The interviewer will usually guide you.

What if the interviewer keeps interrupting my flow?

Adapt. The interviewer might be short on time or wants to focus on something specific. Follow their lead while still demonstrating the qualities they're looking for.

Should I ask for hints if I'm stuck?

Yes, it's better than sitting in silence. Say: "I'm thinking about how to handle [X]. Do you have a preference for which direction I should explore?" This shows self-awareness and collaboration.

How do I practice avoiding these mistakes?

Record yourself doing mock interviews. Watch the recording and check for each mistake. It's uncomfortable but incredibly effective. Most people discover they make mistakes they didn't realize.