Practice
1. A global CDN operator needs to prevent a misconfiguration from propagating incorrect BGP routes that could cause widespread internet outages. Which component is primarily responsible for detecting and containing such route leaks?
easy
Solution
Step 1: Identify BGP route filtering role
BGP route filters and prefix limits on edge routers prevent invalid or excessive route announcements, containing route leaks.Step 2: Eliminate other options
DNS firewalls (A) do not control BGP routing; client-side validation (C) is ineffective for routing control; distributed caches (D) do not enforce routing policies.Final Answer:
Option B -> Option BQuick Check:
BGP route filters contain route leaks at the network edge [check]
Hint: Edge routers enforce BGP filters to block bad routes [check]
Common Mistakes:
2. In the booking flow, at step 4 the Availability Service checks the calendar cache before confirming a booking. What happens if the cache entry for the requested dates is stale or missing at this step?
easy
Solution
Step 1: Trace Availability Service behavior at step 4
The service uses cache for fast reads but must ensure correctness before booking confirmation.Step 2: Handle stale or missing cache entries
Rejecting immediately (A) reduces UX; optimistic booking (C) risks double bookings; async delay (D) hurts latency. Synchronous DB query (B) ensures correctness.Final Answer:
Option B -> Option BQuick Check:
Fallback to DB ensures accurate availability check on cache miss [check]
Hint: Cache miss triggers synchronous DB check for correctness [check]
Common Mistakes:
3. In a graph database request flow, at step 4 the query engine evaluates the traversal depth limit. What happens if the depth limit is reached before finding the target node?
easy
Solution
Step 1: Understand traversal depth limit role
The depth limit caps how far the traversal explores to avoid excessive computation.Step 2: Behavior at limit
When the limit is reached, traversal stops and returns partial results found so far, not an error or restart.Final Answer:
Option A -> Option AQuick Check:
Traversal depth limit stops traversal and returns partial results [check]
Hint: Traversal stops at depth limit, returns partial results [check]
Common Mistakes:
4. If the metrics aggregation service fails to update the error budget in real time, what is the most likely failure observed by the alerting system?
medium
Solution
Step 1: Understand failure impact on error budget updates
If aggregation service fails partially, inconsistent error counts across nodes cause alerting to behave erratically.Step 2: Eliminate other failure modes
Resetting to zero (A) is unlikely without explicit reset; no alerts (B) implies total failure, not partial inconsistency; delayed alerts (D) imply buffering, not aggregation failure.Final Answer:
Option A -> Option AQuick Check:
Aggregation failure -> inconsistent counts -> random alert triggers [check]
Hint: Aggregation failure causes inconsistent counts, leading to random alerts [OK]
Common Mistakes:
5. At 10x scale of concurrent users, which component in a typeahead autocomplete system using Elasticsearch and Trie is most likely to become the bottleneck first, and why?
hard
Solution
Step 1: Analyze statefulness and scalability
Trie servers hold large in-memory state and are difficult to shard due to pointer-based node structures, limiting horizontal scaling.Step 2: Compare other components
Elasticsearch scales horizontally with shards; prefix hash map can be partitioned or replaced; client UI is less likely the first bottleneck at backend scale.Final Answer:
Option A -> Option AQuick Check:
Trie statefulness limits sharding -> bottleneck at scale [check]
Hint: Trie stateful, hard to shard -> first bottleneck at scale [check]
Common Mistakes: