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A data science team has trained a large deep learning model using Vertex AI Workbench. They want to deploy it to Vertex AI Prediction for online serving. The model is stored in a custom container with a Python-based web server. Which TWO actions should the team take to ensure optimal performance and cost?
Option B is correct because a large deep learning model benefits from GPU acceleration, and Vertex AI Prediction supports attaching NVIDIA GPU machine types (e.g., n1-standard-4 with T4 or A100) to prediction nodes, which dramatically reduces inference latency for compute-heavy n…Read full explanation
Which THREE actions should be taken to automate a machine learning pipeline using Cloud Build and Vertex AI?
Option A is correct because a cloudbuild.yaml file is the declarative configuration Cloud Build uses to define build steps, and one of those steps can build a custom training container image (e.g., via docker build) and then submit a Vertex AI PipelineJob using the gcloud ai pipe…Read full explanation
A company has deployed a TensorFlow model on Vertex AI Prediction for real-time inference. They notice that during peak hours, the prediction latency increases significantly, and some requests time out. The model requires GPU acceleration. Which action should they take to reduce latency and avoid timeouts?
Enabling autoscaling with appropriate min and max replicas allows the endpoint to dynamically scale up during peak traffic and scale down during low traffic, ensuring sufficient GPU resources to handle the load without manual intervention. Ensuring adequate GPU quota is also crit…Read full explanation
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