Exam Professional Machine Learning Engineer topic 1 question 262 discussion

Agree with bobjr

https://cloud.google.com/vertex-ai/docs/featurestore/managing-featurestores Specifically mentioned here that --> If CPU utilization is consistently high, consider increasing the number of online serving nodes for your featurestore.

Gemini + Perplexity ai + ChatGPT votes A Because : B. Enable Autoscaling: While autoscaling can be useful, it might not react quickly enough to sudden spikes in traffic during batch ingestion. Scheduling the increase ensures that the resources are available when needed.

This question is valid for the Legacy feature store. https://cloud.google.com/vertex-ai/docs/featurestore/ingesting-batch#import_job_performance

https://cloud.google.com/vertex-ai/docs/featurestore/managing-featurestores?&_gl=1*sswg5e*_ga*NDE2OTc3OTAzLjE3MDU4OTQ5OTE.*_ga_WH2QY8WWF5*MTcwNTkzNDM0NS40LjAuMTcwNTkzNDM0NS4wLjAuMA..&_ga=2.242492743.-416977903.1705894991#online_serving_nodes

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.

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.