Practice Test 2 | Google Cloud Certified Professional Data Engineer | Dumps | Mock Test
You have deployed a Tensorflow machine learning model using Cloud Machine Learning Engine. The model should be able to handle high volume of instances in a job to run complex models. The model should also write the output to Google Storage.
Which of the following approaches is recommended?
A. Use online prediction when using the model. Batch prediction supports asynchronous requests.
B. Use batch prediction when using the model. Batch prediction supports asynchronous requests.
C. Use batch prediction when using the model to return the results as soon as possible.
D. Use online prediction when using the model to return the results as soon as possible.
Answer: B.
AI Platform provides two ways to get predictions from trained models: online prediction (sometimes called HTTP prediction), and batch prediction. In both cases, you pass input data to a cloud-hosted machine-learning model and get inferences for each data instance. The differences are shown in the following table:
Batch prediction can handle high volume of instances in a job to run complex models. It also writes the output to Google Storage by specified location.
Answer A & D are incorrect: Online prediction doesn’t support handling high volume of instances per job and doesn’t write output to Google Storage.
Answer C is incorrect: Batch prediction doesn’t return the output as soon as possible, it supports asynchronous requests.
Source(s):
Online vs. Batch Prediction: https://cloud.google.com/ml-engine/docs/tensorflow/online-vs-batch- prediction
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