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Question 100 - Professional Machine Learning Engineer discussion

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You need to design an architecture that serves asynchronous predictions to determine whether a particular mission-critical machine part will fail. Your system collects data from multiple sensors from the machine. You want to build a model that will predict a failure in the next N minutes, given the average of each sensor's data from the past 12hours. How should you design the architecture?

A.
1. HTTP requests are sent by the sensors to your ML model, which is deployed as a microservice and exposes a REST API for prediction 2. Your application queries a Vertex AI endpoint where you deployed your model. 3. Responses are received by the caller application as soon as the model produces the prediction.
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A.
1. HTTP requests are sent by the sensors to your ML model, which is deployed as a microservice and exposes a REST API for prediction 2. Your application queries a Vertex AI endpoint where you deployed your model. 3. Responses are received by the caller application as soon as the model produces the prediction.
B.
1. Events are sent by the sensors to Pub/Sub, consumed in real time, and processed by a Dataflow stream processing pipeline. 2. The pipeline invokes the model for prediction and sends the predictions to another Pub/Sub topic. 3. Pub/Sub messages containing predictions are then consumed by a downstream system for monitoring.
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B.
1. Events are sent by the sensors to Pub/Sub, consumed in real time, and processed by a Dataflow stream processing pipeline. 2. The pipeline invokes the model for prediction and sends the predictions to another Pub/Sub topic. 3. Pub/Sub messages containing predictions are then consumed by a downstream system for monitoring.
C.
1. Export your data to Cloud Storage using Dataflow. 2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data. 3. Export the batch prediction job outputs from Cloud Storage and import them into Cloud SQL.
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C.
1. Export your data to Cloud Storage using Dataflow. 2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data. 3. Export the batch prediction job outputs from Cloud Storage and import them into Cloud SQL.
D.
1. Export the data to Cloud Storage using the BigQuery command-line tool 2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data. 3. Export the batch prediction job outputs from Cloud Storage and import them into BigQuery.
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D.
1. Export the data to Cloud Storage using the BigQuery command-line tool 2. Submit a Vertex AI batch prediction job that uses your trained model in Cloud Storage to perform scoring on the preprocessed data. 3. Export the batch prediction job outputs from Cloud Storage and import them into BigQuery.
Suggested answer: B

Explanation:

Reasoning: The question asks for a design that servesasynchronous predictionsto determine whether a machine part will fail. This means that the predictions do not need to be returned immediately to the sensors, but can be processed in batches and sent to a downstream system for monitoring. Option B is the only one that uses astreamingdata pipeline with Pub/Sub and Dataflow, which can handle real-time data ingestion, processing, and prediction. Option B also invokes the model for prediction, which is required by the question. The other options either usesynchronous predictions(option A),batch predictions(options C and D), or do not invoke the model for prediction (option D).

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asked 18/09/2024
Joshua Paffen
20 questions
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