Practice
1. In the webhook delivery system, at Step 4 the Delivery Worker checks the subscriber's last acknowledged event ID before sending the next event. What state does the Delivery Worker hold if the subscriber's acknowledgement is delayed beyond the retry timeout?
easy
Solution
Step 1: Trace Delivery Worker decision at Step 4 -- subscriber ack delay
The worker waits for acknowledgement; if delayed beyond retry timeout, it escalates failure to alerting for manual intervention.Step 2: Eliminate other options
Marking as delivered without ack breaks reliability; deleting event loses data; scheduling retry is normal but after multiple failures escalation is needed.Final Answer:
Option A -> Option AQuick Check:
Delayed ack beyond retry timeout -> escalate alert [check]
Hint: Delayed ack triggers alert escalation [check]
Common Mistakes:
2. In a relational database transaction flow, at Step 4 the transaction manager checks the lock table before committing. What happens if the lock is held by another transaction at this step?
easy
Solution
Step 1: Understand lock table role at Step 4
The transaction manager uses the lock table to ensure no conflicting writes before commit.Step 2: Behavior on lock contention
If the lock is held, the transaction manager blocks to maintain serializability, waiting or timing out rather than aborting immediately or skipping checks.Final Answer:
Option B -> Option BQuick Check:
Lock contention causes blocking until release or timeout [check]
Hint: Lock held -> transaction blocks, no skipping or immediate abort [check]
Common Mistakes:
3. At 10x transaction volume, which component of a relational database system is most likely to become the bottleneck first, and why?
hard
Solution
Step 1: Identify bottleneck under high concurrency
Lock manager handles concurrency control; at 10x load, lock contention increases exponentially.Step 2: Compare other components
Query optimizer scales well with caching; storage and network scale with hardware upgrades; lock manager contention is harder to mitigate.Final Answer:
Option C -> Option CQuick Check:
Lock manager contention -> first bottleneck at high transaction scale [check]
Hint: Lock manager contention grows fastest at scale [check]
Common Mistakes:
4. At 10x scale, which component in the real-time ML inference system is most likely to fail first and why?
hard
Solution
Step 1: Analyze scaling bottlenecks
Model storage (A) scales with caching; Kafka (C) can be scaled by adding partitions; load balancers (D) scale horizontally.Step 2: Identify CPU bottleneck in inference
Prediction API servers (B) perform CPU/GPU-bound inference; scaling is limited by hardware and model complexity, causing first failure under 10x load.Final Answer:
Option D -> Option DQuick Check:
Inference servers hit CPU/GPU limits before storage or Kafka scale [check]
Hint: Inference servers hit CPU/GPU bottleneck before other components [OK]
Common Mistakes:
5. At 10x scale, which component in a server-side service discovery architecture is most likely to fail first and why?
hard
Solution
Step 1: Identify bottlenecks in server-side discovery
API Gateway queries registry per request without caching, causing query amplification at scale.Step 2: Compare other components
Registry is centralized but can be scaled with sharding; microservices scale horizontally; load balancers handle instance scaling better than API Gateway query load.Final Answer:
Option C -> Option CQuick Check:
API Gateway query volume grows linearly causing first failure [check]
Hint: API Gateway query volume grows fastest causing first failure [check]
Common Mistakes: