Exam Professional Machine Learning Engineer topic 1 question 187 discussion

Autoscaling based on baseline: Vertex AI endpoints have built-in autoscaling capabilities. Setting a minReplicaCount ensures there are always at least that many replicas running, handling the baseline traffic efficiently. When demand increases above the baseline, autoscaling will automatically provision additional replicas to maintain performance. Efficient scaling: This approach allows the endpoint to scale up smoothly as traffic increases, preventing sudden spikes in latency for users. Targeted resource allocation: Unlike option A (deploying multiple models), this method avoids redundant resources when traffic is low. Additionally, option D (switching to GPUs) might be unnecessary if the bottleneck isn't processing power.

My Answer B The letter C would be the correct answer if the target were set lower to anticipate traffic spikes, not set higher as the answer says. However, considering that the minReplicaCount is now twice the known value, letter B is the most appropriate answer as it suggests considering setting a new minReplicaCount, which could be the best choice.

Not C, if set to a higher value, it is less easier to autoscale to another instance, as it will wait the utilisation to a even higher value.

I go with C. It calculates the number of replicas based on CPU utilization. https://cloud.google.com/python/docs/reference/aiplatform/latest/google.cloud.aiplatform_v1.types.AutoscalingMetricSpec

B This option allows you to leverage the power and simplicity of Vertex AI to automatically scale your endpoint resources according to the traffic patterns.

c as it is dynamic