Practice
1. In the log aggregation pipeline, at Step 4 the Log Processor reads from Kafka and enriches logs with metadata. What happens if the enrichment service is temporarily unavailable at this step?
easy
Solution
Step 1: Trace Log Processor behavior at Step 4
It reads logs from Kafka and enriches them before forwarding.Step 2: Understand failure handling
If enrichment service is down, buffering and retrying prevents data loss and ensures enrichment.Final Answer:
Option A -> Option AQuick Check:
Buffer and retry enrichment -> no data loss or offset commit [check]
Hint: Buffer and retry enrichment to avoid data loss [check]
Common Mistakes:
2. To support personalized course recommendations at scale, should the system use a batch offline ML pipeline or a real-time streaming recommendation engine? Consider latency and update frequency constraints.
medium
Solution
Step 1: Analyze latency and update frequency constraints
Real-time streaming (Options C and D) offers low latency but may sacrifice model complexity or accuracy.Step 2: Evaluate batch offline pipeline suitability
Batch offline ML (Use batch offline ML pipeline for periodic recommendations with high accuracy) provides higher accuracy with periodic updates, suitable when real-time freshness is not critical.Final Answer:
Option D -> Option DQuick Check:
Batch offline pipeline balances accuracy and periodic updates [check]
Hint: Batch ML for accuracy, streaming for latency trade-off [OK]
Common Mistakes:
3. If the health monitoring component of the GSLB fails silently, what is the most likely failure observed by end users?
medium
Solution
Step 1: Understand health monitor role
The health monitor informs GSLB which data centers are healthy; without it, GSLB cannot avoid unhealthy endpoints.Step 2: Analyze failure impact
Without health info, GSLB routes users to unhealthy data centers causing failures (Users are routed to unhealthy data centers causing request failures or timeouts). Increased latency (Users experience increased latency due to rerouting through backup data centers) or DNS failures (DNS queries fail to resolve, causing complete service outage) are indirect or unlikely. Even traffic distribution (Traffic is evenly distributed regardless of data center health, causing overload) ignores health but is less precise than routing to unhealthy nodes.Final Answer:
Option A -> Option AQuick Check:
Silent health monitor failure -> routing to unhealthy data centers [check]
Hint: No health data -> users sent to unhealthy data centers [check]
Common Mistakes:
4. At 10x scale, which component in a Kafka-like distributed message queue is most likely to fail first, and why?
hard
Solution
Step 1: Identify bottlenecks at 10x scale
Network switches (A) scale with hardware upgrades; ZooKeeper (C) handles coordination but is lightweight compared to brokers; consumers (D) can be scaled horizontally.Step 2: Kafka broker partition leader overload
Partition leaders handle all writes and replication for their partitions; at 10x scale, leaders become hotspots causing CPU, disk, and network saturation, leading to failures.Final Answer:
Option A -> Option AQuick Check:
Partition leaders overloaded -> broker failure first at scale [check]
Hint: Partition leaders handle all writes -> overload first at scale [check]
Common Mistakes:
5. At 10x scale, which component in a durable messaging system fails first and why?
hard
Solution
Step 1: Identify bottlenecks at 10x scale
Databases (A) can be scaled with sharding; delivery workers (C) can be horizontally scaled; chat servers (D) are stateful but not the first to fail.Step 2: Analyze Kafka broker load
Kafka brokers handle replication and disk I/O per partition leader; at 10x scale, leaders become overloaded causing broker failures first.Final Answer:
Option B -> Option BQuick Check:
Kafka partition leaders overloaded with replication cause first failure at scale [check]
Hint: Kafka partition leaders hit replication I/O bottleneck first at scale [check]
Common Mistakes: