Google Exam Professional Machine Learning Engineer Topic 6 Question 66 Discussion

Actual exam question for Google's Professional Machine Learning Engineer exam

Question #: 66
Topic #: 6

[All Professional Machine Learning Engineer Questions]

You work for a social media company. You want to create a no-code image classification model for an iOS mobile application to identify fashion accessories You have a labeled dataset in Cloud Storage You need to configure a training workflow that minimizes cost and serves predictions with the lowest possible latency What should you do?

ATrain the model by using AutoML, and register the model in Vertex Al Model Registry Configure your mobile
application to send batch requests during prediction.

BTrain the model by using AutoML Edge and export it as a Core ML model Configure your mobile application
to use the mlmodel file directly.

CTrain the model by using AutoML Edge and export the model as a TFLite model Configure your mobile application to use the tflite file directly

DTrain the model by using AutoML, and expose the model as a Vertex Al endpoint Configure your mobile application to invoke the endpoint during prediction.

Show Suggested Answer

Suggested Answer: D

Applying quantization to your SavedModel by reducing the floating point precision can help reduce the serving latency by decreasing the amount of memory and computation required to make a prediction. TensorFlow provides tools such as the tf.quantization module that can be used to quantize models and reduce their precision, which can significantly reduce serving latency without a significant decrease in model performance.

by Lai at Oct 22, 2023, 12:31 PM

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