Practice
1. A Pastebin-like service must store millions of short text snippets with automatic expiration after a configurable TTL. Which storage component best handles this requirement efficiently?
easy
Solution
Step 1: Identify storage with native TTL support
Redis supports automatic expiration of keys with TTL, avoiding manual cleanup overhead.Step 2: Evaluate alternatives
Relational DB with batch jobs (A) adds latency and complexity; file systems (C) lack efficient expiration; NoSQL without TTL (D) requires expensive filtering.Final Answer:
Option D -> Option DQuick Check:
Redis TTL automates expiration, reducing operational complexity [check]
Hint: Use storage with native TTL for automatic expiration [check]
Common Mistakes:
2. A microservices architecture requires low-latency, strongly typed RPC calls between internal services with automatic code generation. Which technology best fits this requirement?
easy
Solution
Step 1: Identify serialization and transport needs
Low-latency and strongly typed RPC require efficient binary serialization and HTTP/2 transport.Step 2: Evaluate technology fit
gRPC uses Protocol Buffers for compact binary serialization and HTTP/2 for multiplexed streams, meeting requirements. REST with JSON is flexible but slower and less type-safe. GraphQL is query-focused, not RPC. SOAP is verbose and outdated.Final Answer:
Option D -> Option DQuick Check:
gRPC + Protobuf -> low-latency, strongly typed RPC [check]
Hint: Binary + HTTP/2 + codegen = gRPC [check]
Common Mistakes:
3. A production system needs to continuously measure its reliability against defined error budgets and alert when the budget is close to exhaustion. Which component is primarily responsible for aggregating error rates and calculating remaining error budget in real time?
easy
Solution
Step 1: Identify the component responsible for real-time error budget calculation
The metrics aggregation service continuously processes incoming metrics to compute SLO compliance and error budget burn rates, enabling timely alerts.Step 2: Eliminate other options based on their roles
Kafka stores raw data but does not compute error budgets in real time; tracing systems focus on request-level diagnostics, not aggregate error rates; dashboards visualize data but do not perform calculations.Final Answer:
Option B -> Option BQuick Check:
Metrics aggregation service -> real-time error budget tracking [check]
Hint: Metrics aggregation calculates error budgets in real time [OK]
Common Mistakes:
4. A high-throughput web service must handle millions of requests per second with minimal latency and no session affinity. Which architectural component best supports this requirement?
easy
Solution
Step 1: Identify stateless server benefits
Stateless servers allow horizontal scaling without session affinity, reducing latency and complexity.Step 2: Evaluate session management
External session storage decouples session state from servers, enabling load balancer to distribute requests evenly.Final Answer:
Option B -> Option BQuick Check:
Stateless servers + external session store -> scalable, low-latency [check]
Hint: Stateless servers + external session store scale best [check]
Common Mistakes:
5. For storing user and item embeddings in a recommendation system, which storage choice best balances low-latency reads and frequent updates at scale?
medium
Solution
Step 1: Identify storage requirements
Embedding storage requires low-latency reads and frequent writes at scale with high availability.Step 2: Evaluate storage options
Relational DB (A) struggles with write throughput and scaling; Cassandra (B) offers tunable consistency but may add complexity; file system batch loading (D) introduces stale data and high latency. Document store (C) supports flexible schema and fast reads with embedded documents, balancing latency and update frequency.Final Answer:
Option A -> Option AQuick Check:
Document store balances flexible schema and read/write latency [check]
Hint: Document store balances schema flexibility and latency [check]
Common Mistakes: