An ML engineer is monitoring a Vertex AI Feature Store used for online serving. Which metrics are most important to track for ensuring low-latency online serving?
These directly affect online serving performance.
Why this answer
For online serving, the primary concern is the latency and reliability of feature value retrieval at inference time. The p99 retrieval latency directly measures the worst-case delay experienced by users, while the error rate captures failures that could cause serving disruptions. Other metrics like storage utilization or batch export duration are relevant for offline or batch pipelines, not real-time serving.
Exam trap
The trap here is that candidates confuse metrics for offline batch operations (like export duration) with those for online serving, or assume that storage-level metrics (like utilization) are sufficient for performance monitoring, when in fact only retrieval latency and error rate directly reflect the serving quality.
How to eliminate wrong answers
Option A is wrong because the number of feature stores and feature values does not directly impact serving latency; it is a capacity planning metric, not a performance indicator. Option B is wrong because storage utilization and write throughput are important for data ingestion and maintenance, but they do not measure the online retrieval performance that affects inference latency. Option C is wrong because batch export duration and number of exported features pertain to offline batch serving or data export jobs, not the low-latency online serving path.