Google Cloud Professional Data Engineer — Question 39

You work for a shipping company that has distribution centers where packages move on delivery lines to route them properly. The company wants to add cameras to the delivery lines to detect and track any visual damage to the packages in transit. You need to create a way to automate the detection of damaged packages and flag them for human review in real time while the packages are in transit. Which solution should you choose?

Answer options

• A. Use BigQuery machine learning to be able to train the model at scale, so you can analyze the packages in batches.
• B. Train an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.
• C. Use the Cloud Vision API to detect for damage, and raise an alert through Cloud Functions. Integrate the package tracking applications with this function.
• D. Use TensorFlow to create a model that is trained on your corpus of images. Create a Python notebook in Cloud Datalab that uses this model so you can analyze for damaged packages.

Correct answer: B

Explanation

The correct answer is B because training an AutoML model on your images allows for customized learning specifically for detecting damage, and building an API facilitates easy integration with existing systems. While option A focuses on batch processing, it does not provide real-time detection. Option C, while using a powerful tool, may not be as adaptable for specific damage detection as a custom AutoML model. Option D involves more complexity with TensorFlow and might not achieve the same level of integration and real-time alerting as option B.