Google Professional Machine Learning Engineer
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Ultimate access to all questions.
You are working at a semiconductor manufacturing company and tasked with developing a real-time application to automate the quality control process. At the end of the assembly line, high-definition images of each semiconductor are captured in real time. These photos, along with accompanying tabular data containing each semiconductor’s batch number, serial number, dimensions, and weight, are uploaded to a Cloud Storage bucket. Your goal is to configure the model training and serving setup to maximize model accuracy. What should you do?
A
Use Vertex AI Data Labeling Service to label the images, and train an AutoML image classification model. Deploy the model, and configure Pub/Sub to publish a message when an image is categorized into the failing class.
B
Use Vertex AI Data Labeling Service to label the images, and train an AutoML image classification model. Schedule a daily batch prediction job that publishes a Pub/Sub message when the job completes.
C
Convert the images into an embedding representation. Import this data into BigQuery, and train a BigQuery ML K-means clustering model with two clusters. Deploy the model and configure Pub/Sub to publish a message when a semiconductor’s data is categorized into the failing cluster.
D
Import the tabular data into BigQuery, use Vertex AI Data Labeling Service to label the data and train an AutoML tabular classification model. Deploy the model, and configure Pub/Sub to publish a message when a semiconductor’s data is categorized into the failing class.