Google Exam Professional Machine Learning Engineer Topic 6 Question 71 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam

Question #: 71
Topic #: 6

[All Professional Machine Learning Engineer Questions]

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?

ASchedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs.

BEnable autoscaling of the online serving nodes in your featurestore

CEnable autoscaling for the prediction nodes of your DeployedModel in the Vertex Al endpoint.

DIncrease the worker counts in the importFeaturevalues request of your batch ingestion job.

Show Suggested Answer

Suggested Answer: B

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

by Cecilia at Feb 11, 2024, 04:02 PM

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Sanjuana

6 months ago

I think scheduling an increase in the number of online serving nodes could also help.

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Slyvia

6 months ago

I disagree. I believe increasing the worker counts in the batch ingestion job would be more effective.

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Isadora

7 months ago

That sounds like a good idea. It could help improve performance during the batch ingestion.

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Larue

7 months ago

I think we should enable autoscaling of the online serving nodes.

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Jina

8 months ago

But what about option D? Increasing the worker counts in the batch ingestion job could also help distribute the load and reduce the impact on online serving, no?

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Walton

7 months ago

D) Increasing the worker counts in the batch ingestion job could also help distribute the load and reduce the impact on online serving.

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Vi

8 months ago

A) Schedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs.

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Percy

8 months ago

D) Increase the worker counts in the importFeaturevalues request of your batch ingestion job.

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Shelba

8 months ago

C) Enable autoscaling for the prediction nodes of your DeployedModel in the Vertex AI endpoint.

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Odette

8 months ago

B) Enable autoscaling of the online serving nodes in your featurestore

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Mitzie

8 months ago

A) Schedule an increase in the number of online serving nodes in your featurestore prior to the batch ingestion jobs.

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Maia

8 months ago

I'm leaning towards option B, enabling autoscaling of the online serving nodes. That should help the featurestore handle the increased load without us having to manually adjust the node count.

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Sherly

8 months ago

Ha, imagine if we had to manually adjust the worker counts every time. 'Okay, everyone, stop what you're doing, it's time for the daily batch ingestion! Quick, someone count the nodes and tell me how many workers we need!'

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Corrinne

8 months ago

Hmm, I'm not sure. Autoscaling seems like the more elegant solution to me. I don't want to have to manually adjust the worker counts every time we have a batch ingestion job.

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