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Question 232 - Professional Machine Learning Engineer discussion

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You recently deployed a model lo a Vertex Al endpoint and set up online serving in Vertex Al 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 featurestores 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?

A.
Schedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs.
Answers
A.
Schedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs.
B.
Enable autoscaling of the online serving nodes in your featurestore
Answers
B.
Enable autoscaling of the online serving nodes in your featurestore
C.
Enable autoscaling for the prediction nodes of your DeployedModel in the Vertex Al endpoint.
Answers
C.
Enable autoscaling for the prediction nodes of your DeployedModel in the Vertex Al endpoint.
D.
Increase the worker counts in the importFeaturevalues request of your batch ingestion job.
Answers
D.
Increase the worker counts in the importFeaturevalues request of your batch ingestion job.
Suggested answer: B

Explanation:

Vertex AI Feature Store provides two options for online serving: Bigtable and optimized online serving. Both options support autoscaling, which means that the number of online serving nodes can automatically adjust to the traffic demand. By enabling autoscaling, you can improve the online serving performance and reduce the feature retrieval latency during the daily batch ingestion. Autoscaling also helps you optimize the cost and resource utilization of your featurestore.Reference:

Online serving | Vertex AI | Google Cloud

New Vertex AI Feature Store: BigQuery-Powered, GenAI-Ready | Google Cloud Blog

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asked 18/09/2024
DIGIX srl
32 questions
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