Practice
1. A real-time ML inference system must serve predictions with minimal latency while allowing safe model updates without downtime. Which component best enables seamless model rollout with instant rollback capability?
easy
Solution
Step 1: Identify deployment strategies for safe model updates
Batch inference (C) is offline and adds latency, so it doesn't support real-time rollout. Feature store (D) optimizes input features but doesn't handle rollout or rollback. A/B testing (A) splits live traffic, risking user experience if the new model is faulty.Step 2: Understand shadow mode benefits
Shadow mode (B) runs new models in parallel on live data without affecting production responses, enabling validation and instant rollback if issues arise.Final Answer:
Option A -> Option AQuick Check:
Shadow mode runs new models safely alongside production [check]
Hint: Shadow mode isolates new models without impacting live traffic [OK]
Common Mistakes:
2. In the order matching flow, at step 5 the Matching Engine receives a new buy order for stock XYZ. What state does the Matching Engine hold immediately after processing this order if no matching sell orders exist?
easy
Solution
Step 1: Trace Matching Engine behavior at step 5
When no matching sell orders exist, the buy order is added to the order book's buy side and marked as open, waiting for future matches.Step 2: Eliminate other options
The order is marked as matched and removed from the order book is wrong because no match occurred; The order is rejected due to lack of matching sell orders is wrong because orders are not rejected for no matches; The order is forwarded to the Market Data Feed before being stored is wrong because Market Data Feed only publishes after matching or order book update.Final Answer:
Option B -> Option BQuick Check:
Unmatched order -> added to order book as open [check]
Hint: Unmatched orders stay open in order book [check]
Common Mistakes:
3. For a system requiring extremely low false negatives and exact key existence checks at scale, which approach is best?
medium
Solution
Step 1: Analyze false negative tolerance
Bloom filters have zero false negatives but allow false positives, so exact checks need backend confirmation.Step 2: Evaluate approaches
Only Bloom filter (A) risks false positives causing incorrect hits. Distributed cache (C) is costly at scale. Counting Bloom filters (D) track counts but still have false positives and complexity.Final Answer:
Option D -> Option DQuick Check:
Bloom filter filters negatives; backend confirms positives for exactness [check]
Hint: Bloom filter filters negatives; backend confirms positives [check]
Common Mistakes:
4. At 10x scale, which component in a content negotiation versioning system is most likely to become the bottleneck, and why?
hard
Solution
Step 1: Identify component handling content negotiation
The API gateway parsesAcceptheaders to route requests to correct versioned backend.Step 2: Analyze scaling impact
Parsing headers is CPU-intensive and cannot leverage caching well, causing gateway CPU to saturate first at high request volumes.Step 3: Compare other components
Backends scale horizontally; CDNs struggle but can be configured; client SDKs are not server bottlenecks.Final Answer:
Option B -> Option BQuick Check:
Gateway CPU parsing Accept headers saturates first at scale [check]
Hint: Gateway CPU parsing Accept headers saturates first [check]
Common Mistakes:
5. At 10x scale, which component in a synchronous quorum replication system is most likely to become the bottleneck and why?
hard
Solution
Step 1: Identify synchronous quorum coordinator role
Coordinator waits synchronously for majority acks, causing latency and throughput limits.Step 2: Compare other components' scaling challenges
Storage nodes and network scale better; client-side blocking is per client, less centralized.Final Answer:
Option A -> Option AQuick Check:
Coordinator sync wait limits throughput at scale [check]
Hint: Coordinator sync wait blocks throughput first at scale [check]
Common Mistakes: