Professional Machine Learning Engineer Exam - Question 272

Question

You work for a semiconductor manufacturing company. You need to create a real-time application that automates the quality control process. High-definition images of each semiconductor are taken at the end of the assembly line in real time. The photos are uploaded to a Cloud Storage bucket along with tabular data that includes each semiconductor’s batch number, serial number, dimensions, and weight. You need to configure model training and serving while maximizing model accuracy. What should you do?

Examice · Accepted Answer

To create a real-time application for quality control in a semiconductor manufacturing process, using high-definition images to identify defects is crucial. The best approach is to utilize image classification models, as they are tailored to handle and categorize visual data effectively. Vertex AI Data Labeling Service can be used to label the images appropriately, and an AutoML image classification model can be trained to identify and categorize the images into 'passing' or 'failing' classes. Upon categorizing an image into the failing class, configuring Pub/Sub to publish a message ensures immediate notification and prompt quality control actions, making the process real-time and efficient. Hence, leveraging an image classification model with real-time Pub/Sub notifications is the optimal solution.

b1a8fae · Answer

I go with A.

omermahgoub · Answer

Real-time Processing, uploading images to Cloud Storage triggers the AutoML image classification model for immediate processing, enabling real-time quality control decisions.
Image Classification, the scenario focuses on classifying images as "passing" or "failing" quality, making image classification the appropriate approach.
Pub/Sub Notifications, Pub/Sub messaging efficiently alerts downstream systems about failing classifications, allowing for prompt quality control actions.

daidai75 · Answer

The right answer should be A

pikachu007 · Answer

Option B: Batch prediction jobs introduce latency, making them unsuitable for real-time quality control.
Option C: K-means clustering is an unsupervised learning technique that doesn't leverage labeled data to distinguish between passing and failing semiconductors, potentially compromising accuracy.
Option D: Tabular classification focuses on structured data, not images, and might overlook visual defects captured in the photos.

AzureDP900 · Answer

Option A is correct
The high-definition images of each semiconductor are taken in real-time at the end of the assembly line.
The images are uploaded to Cloud Storage along with tabular data that includes batch number, serial number, dimensions, and weight.
You need to configure model training and serving while maximizing model accuracy.

Professional Machine Learning Engineer Exam - Question 272

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