Practice
1. In the alerting system's request flow, at Step 4 the Composite Alert Processor receives individual alerts from Threshold and Anomaly detectors. What does it do when one of the input alerts is delayed beyond its TTL?
easy
Solution
Step 1: Understand Composite Alert Processor behavior
The processor combines multiple alerts but must handle delayed inputs gracefully to avoid blocking.Step 2: TTL handling
When an input alert exceeds its TTL, it is considered stale; the processor evaluates the composite alert with the alerts it has rather than waiting indefinitely.Final Answer:
Option D -> Option DQuick Check:
Composite alert evaluates with stale inputs marked, not blocking indefinitely [check]
Hint: Delayed alert -> mark stale, evaluate with available inputs [check]
Common Mistakes:
2. A notification system must handle 1 million notification writes per second and fan out to millions of devices asynchronously. Which component best decouples the write load from delivery workers to ensure durability and scalability?
easy
Solution
Step 1: Identify the role of the message queue
Kafka provides durable, partitioned logs that decouple producers from consumers, enabling high write throughput and ordered delivery per user.Step 2: Evaluate other options
Relational DB polling (A) can't handle 1M writes/sec efficiently; Redis (C) lacks durability and persistence guarantees; synchronous HTTP (D) blocks on delivery and doesn't scale.Final Answer:
Option D -> Option DQuick Check:
Kafka partitions by user ID -> durable, ordered fan-out [check]
Hint: Kafka partitions decouple writes from delivery at scale [check]
Common Mistakes:
3. Without the message queue (Kafka) in the architecture, what exactly breaks when Alice sends a message to Bob during a partial regional outage?
medium
Solution
Step 1: Understand Kafka's role in decoupling
Kafka buffers messages asynchronously, preventing blocking and enabling retries during outages.Step 2: Analyze failure without Kafka
Without Kafka, synchronous calls block sender until receiver responds; if receiver crashes, message is lost with no retry (B). However, buffering on client (C) is incorrect as buffering is server-side.Final Answer:
Option A -> Option AQuick Check:
No Kafka -> synchronous send blocks and message loss on receiver crash [check]
Hint: No Kafka -> send blocks, no retry, message lost on crash [check]
Common Mistakes:
4. If the global BGP route monitoring system is disabled during a misconfiguration event, what is the most likely failure observed in the network recovery process?
medium
Solution
Step 1: Role of BGP monitoring
BGP monitoring detects anomalies and route changes; without it, routers may silently drop updates due to misconfigurations.Step 2: Eliminate other options
Delayed alerts (A) affect operators but don't cause silent drops; routers don't revert to static routes automatically (B); immediate client failover (D) unlikely without proper routing.Final Answer:
Option B -> Option BQuick Check:
Without monitoring, routers drop BGP updates silently causing partial visibility [check]
Hint: No monitoring -> silent BGP update drops, partial routes [check]
Common Mistakes:
5. At 10x scale, which component of a distributed token bucket rate limiter is most likely to fail first and why?
hard
Solution
Step 1: Identify centralized token refill scheduler role
It coordinates token replenishment globally, requiring strong consistency and frequent updates.Step 2: Analyze scaling impact
At 10x scale, centralized scheduler becomes a contention bottleneck and latency hotspot, limiting throughput.Step 3: Compare other components
Distributed cache scales better with sharding; client-side buckets are local and scale naturally; network issues are secondary to scheduler bottleneck.Final Answer:
Option B -> Option BQuick Check:
Centralized scheduler bottlenecks first at scale [check]
Hint: Centralized scheduler bottlenecks first at scale [check]
Common Mistakes: