Practice
1. In a 45-minute system design answer, at step 4 you have identified key components and now start sketching data flow. What happens at this step in the component trace?
easy
Solution
Step 1: Trace the role of step 4 -- sketching data flow
This step validates how components interact and ensures data moves correctly, critical for system correctness.Step 2: Exclude premature or unrelated actions
Finalizing schema or coding before data flow understanding risks misalignment; choosing cloud providers is premature.Final Answer:
Option A -> Option AQuick Check:
Data flow mapping -> validates component interaction [check]
Hint: Map data flow before design details [check]
Common Mistakes:
2. In the TLS handshake trace, at step 4 the server sends its certificate chain to the client. What does the client do immediately after receiving this chain at step 5?
easy
Solution
Step 1: Understand client certificate validation
After receiving the server's certificate chain, the client validates it against its trusted root certificate authorities to ensure authenticity.Step 2: Eliminate incorrect options
The client immediately sends encrypted application data without verification is wrong because the client must verify before sending encrypted data. The client requests a new certificate from the server is incorrect; clients do not request certificates from servers. The client closes the connection if the certificate is expired without any retries is partially true but the client may attempt other steps like checking revocation before closing.Final Answer:
Option D -> Option DQuick Check:
Client verifies certificate chain before proceeding [check]
Hint: Client verifies certificate chain before encrypted data [check]
Common Mistakes:
3. In a system using Kafka for write-ahead logging to ensure strong consistency, what exactly breaks if Kafka becomes unavailable during a write operation?
medium
Solution
Step 1: Trace failure impact on write path
Without Kafka, writes accepted locally but not durably logged can be lost if the server crashes before Kafka recovers.Step 2: Eliminate other options
The system falls back to eventual consistency, causing stale reads temporarily is incorrect because fallback to eventual consistency is not automatic; The client's write blocks indefinitely until Kafka recovers, preventing data loss is wrong because writes may not block indefinitely; The system switches to causal consistency mode automatically to maintain availability is false as consistency mode does not switch automatically.Final Answer:
Option C -> Option CQuick Check:
Writes accepted locally but lost if server crashes before Kafka recovers [check]
Hint: Without Kafka, local writes risk loss if server crashes [check]
Common Mistakes:
4. A team must decide between using a detailed blame-focused report or a blameless timeline with action items for their post-mortem. Which approach is best for fostering continuous improvement in a high-trust engineering culture?
medium
Solution
Step 1: Evaluate cultural impact of blame vs blameless approaches
Blame-focused reports create fear and reduce openness, harming continuous improvement.Step 2: Assess effectiveness of blameless timelines with action items
They foster trust, encourage learning, and drive actionable improvements aligned with high-trust culture.Final Answer:
Option A -> Option AQuick Check:
Blameless + action items -> continuous improvement [check]
Hint: Blameless + action items build trust and improve systems [check]
Common Mistakes:
5. At 10x scale, which component in a PACELC system prioritizing latency under partitions is most likely to fail first, and why?
hard
Solution
Step 1: Identify bottleneck under 10x scale
Local write coordinator handles all writes for its shard and is stateful, limiting horizontal scaling.Step 2: Compare other components
Consensus service (A) is avoided under latency priority; replication queue (C) scales with network; cache (D) invalidation is less critical.Final Answer:
Option B -> Option BQuick Check:
Local write coordinator statefulness -> first bottleneck at scale [check]
Hint: Stateful write coordinator limits scaling first under latency priority [check]
Common Mistakes: