The question presents a scenario involving an AutoML tabular classification model deployed to a Vertex AI endpoint. The model identifies high-value website customers, and the endpoint needs configuration to handle peak traffic during nights and weekends while minimizing latency and cost.
Four options are given, each suggesting different machine type configurations, including the use of GPUs and varying replica counts (minReplicaCount and maxReplicaCount).
The suggested answer is B. This option balances resource utilization and scalability by using a moderately sized machine type (n1-standard-4) and dynamically adjusting the number of replicas based on demand (minReplicaCount=1, maxReplicaCount=8).
You have developed an AutoML tabular classification model that identifies high-value customers who interact with your organization's website. You plan to deploy the model to a new Vertex AI endpoint that will integrate with your website application. You expect higher traffic to the website during nights and weekends. You need to configure the model endpoint's deployment settings to minimize latency and cost. What should you do?
If you often open multiple tabs and struggle to keep track of them, Tabs Reminder is the solution you need. Tabs Reminder lets you set reminders for tabs so you can close them and get notified about them later. Never lose track of important tabs again with Tabs Reminder!
Try our Chrome extension today!
Share this article with your
friends and colleagues.
Earn points from views and
referrals who sign up.
Learn more