Crucial Exams

GCP Professional Cloud Architect Practice Question

A global electronics manufacturer uses high-speed cameras on an on-prem conveyor to capture circuit-board images. They must detect micro-solder defects in near real time, even when the factory WAN link to Google Cloud is occasionally down. The quality team wants a managed Google Cloud service that 1) requires little ML expertise, 2) supports active learning to improve accuracy over time, and 3) can deploy models to an edge appliance for offline inference that later syncs inspection results to BigQuery. Which approach best meets these needs?

  • Use AutoML Vision Object Detection, export the model to TensorFlow Lite, and run inference on an Edge TPU device that later publishes results to BigQuery.

  • Train a custom TensorFlow model in Vertex AI and deploy it on an Anthos bare-metal cluster in the factory.

  • Send each image to the Cloud Vision API over REST and write the responses to Cloud Storage and BigQuery when the link is available.

  • Create a Visual Inspection AI project, train in the cloud, and deploy the model to a Visual Inspection Edge Appliance that syncs results to BigQuery.

GCP Professional Cloud Architect
Ensuring solution and operations excellence
Your Score:
Settings & Objectives
Random Mixed
Questions are selected randomly from all chosen topics, with a preference for those you haven’t seen before. You may see several questions from the same objective or domain in a row.
Rotate by Objective
Questions cycle through each objective or domain in turn, helping you avoid long streaks of questions from the same area. You may see some repeat questions, but the distribution will be more balanced across topics.

Check or uncheck an objective to set which questions you will receive.

Wipe Score
Bash, the Crucial Exams Chat Bot
AI Bot