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Question 28 - Professional Machine Learning Engineer discussion

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You have trained a model on a dataset that required computationally expensive preprocessing operations. You need to execute the same preprocessing at prediction time. You deployed the model on Al Platform for high-throughput online prediction. Which architecture should you use?

A.
* Validate the accuracy of the model that you trained on preprocessed data * Create a new model that uses the raw data and is available in real time * Deploy the new model onto Al Platform for online prediction
Answers
A.
* Validate the accuracy of the model that you trained on preprocessed data * Create a new model that uses the raw data and is available in real time * Deploy the new model onto Al Platform for online prediction
B.
* Send incoming prediction requests to a Pub/Sub topic * Transform the incoming data using a Dataflow job * Submit a prediction request to Al Platform using the transformed data * Write the predictions to an outbound Pub/Sub queue
Answers
B.
* Send incoming prediction requests to a Pub/Sub topic * Transform the incoming data using a Dataflow job * Submit a prediction request to Al Platform using the transformed data * Write the predictions to an outbound Pub/Sub queue
C.
* Stream incoming prediction request data into Cloud Spanner * Create a view to abstract your preprocessing logic. * Query the view every second for new records * Submit a prediction request to Al Platform using the transformed data * Write the predictions to an outbound Pub/Sub queue.
Answers
C.
* Stream incoming prediction request data into Cloud Spanner * Create a view to abstract your preprocessing logic. * Query the view every second for new records * Submit a prediction request to Al Platform using the transformed data * Write the predictions to an outbound Pub/Sub queue.
D.
* Send incoming prediction requests to a Pub/Sub topic * Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic. * Implement your preprocessing logic in the Cloud Function * Submit a prediction request to Al Platform using the transformed data * Write the predictions to an outbound Pub/Sub queue
Answers
D.
* Send incoming prediction requests to a Pub/Sub topic * Set up a Cloud Function that is triggered when messages are published to the Pub/Sub topic. * Implement your preprocessing logic in the Cloud Function * Submit a prediction request to Al Platform using the transformed data * Write the predictions to an outbound Pub/Sub queue
Suggested answer: D

Explanation:

Option A is incorrect because creating a new model that uses the raw data and is available in real time would require retraining the model and deploying it again, which is not efficient or scalable.

Option B is incorrect because using a Dataflow job to transform the incoming data would introduce unnecessary latency and complexity for online prediction, which requires fast and simple processing.

Option C is incorrect because using Cloud Spanner to stream and query the incoming data would incur high costs and overhead for online prediction, which does not need a relational database.

Option D is correct because using a Cloud Function to preprocess the data and submit a prediction request to Al Platform is a simple and scalable solution for online prediction, which leverages the serverless and event-driven features of Cloud Functions.

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asked 18/09/2024
Ghazi Khan
24 questions
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