Topic 1 Question 262
You recently deployed a model to a Vertex AI endpoint and set up online serving in Vertex AI Feature Store. You have configured a daily batch ingestion job to update your featurestore. During the batch ingestion jobs, you discover that CPU utilization is high in your featurestore’s online serving nodes and that feature retrieval latency is high. You need to improve online serving performance during the daily batch ingestion. What should you do?
Schedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs
Enable autoscaling of the online serving nodes in your featurestore
Enable autoscaling for the prediction nodes of your DeployedModel in the Vertex AI endpoint
Increase the worker_count in the ImportFeatureValues request of your batch ingestion job
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- 正解だと思う選択肢: B
Option A: Manually scheduling node increases requires prior knowledge of batch ingestion times and might not be as responsive to unexpected workload spikes. Option C: Autoscaling prediction nodes in the Vertex AI endpoint might help with model prediction latency but doesn't directly address feature retrieval latency from the featurestore. Option D: Increasing worker_count in the batch ingestion job could speed up ingestion but might further strain online serving nodes, potentially worsening latency.
👍 1pikachu0072024/01/13
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