Practice
1. In a time-series ingestion pipeline, at step 4 the Aggregation Worker reads raw event batches from Kafka and writes summarized metrics to the time-series database. What happens if the Aggregation Worker crashes after reading but before writing?
easy
Solution
Step 1: Understand Kafka's offset commit behavior
Kafka retains messages until consumers commit offsets. If the worker crashes before committing, messages remain available for reprocessing.Step 2: Analyze crash timing
Since the worker crashed before writing summaries and committing offsets, raw events remain in Kafka and will be reprocessed after restart.Final Answer:
Option A -> Option AQuick Check:
Kafka retains uncommitted messages for reprocessing [check]
Hint: Kafka commits offsets after processing to avoid data loss [check]
Common Mistakes:
2. For a social network with users having highly variable follower counts (from tens to millions), which fan-out architecture best balances write latency and read latency?
medium
Solution
Step 1: Analyze pure push fan-out (A)
Push to all followers synchronously causes huge write amplification for celebrities, increasing write latency and storage.Step 2: Analyze pure pull fan-out (C)
Pull reduces write cost but increases read latency and complexity for users with many followers.Step 3: Evaluate hybrid fan-out (B)
Hybrid pushes to users with many followers (to reduce read latency) and pulls for users with few followers (to reduce write amplification), balancing both latencies.Step 4: Evaluate push only for active users (D)
Activity-based push doesn't address follower count skew effectively.Final Answer:
Option C -> Option CQuick Check:
Hybrid fan-out balances write and read latency by follower count [check]
Hint: Hybrid fan-out balances write/read latency by follower count [check]
Common Mistakes:
3. Without explicitly stating your assumptions during a design interview, what failure mode is most likely to occur in your explanation?
medium
Solution
Step 1: Understand the role of stating assumptions
Explicit assumptions clarify scope and constraints, preventing misunderstandings.Step 2: Trace failure when assumptions are omitted
Without assumptions, interviewers may accept incomplete designs without challenge, missing flaws.Final Answer:
Option C -> Option CQuick Check:
Omitting assumptions leads to unchallenged flawed designs [check]
Hint: State assumptions to avoid unchallenged flawed designs [check]
Common Mistakes:
4. If the Key Management Service (KMS) becomes unavailable, what exactly breaks in the system's encryption-at-rest workflow?
medium
Solution
Step 1: Understand KMS role in key lifecycle
KMS generates and manages encryption keys; if unavailable, new keys can't be created but existing keys cached locally allow decryption.Step 2: Eliminate incorrect failure modes
Encryption doesn't stop immediately; decryption continues with cached keys; system does not fallback to unencrypted storage automatically.Final Answer:
Option A -> Option AQuick Check:
KMS outage blocks new key generation, existing keys still usable [check]
Hint: KMS down -> no new keys, existing keys still decrypt [check]
Common Mistakes:
5. At 10x scale, which component is most likely to fail first in a system targeting 99.999% availability, and why?
hard
Solution
Step 1: Identify stateful bottlenecks at scale
Chat servers maintain stateful WebSocket connections bound to server memory, limiting horizontal scaling.Step 2: Compare other components' scalability
Databases can shard; load balancers can be scaled horizontally; queues can be partitioned and scaled with disk expansion.Final Answer:
Option D -> Option DQuick Check:
Stateful WebSocket servers -> resharding hard -> first bottleneck at 10x scale [check]
Hint: Stateful WebSocket servers -> resharding hard -> first bottleneck [check]
Common Mistakes: