Topic 1 Question 83

Professional Machine Learning Engineer

Topic 1 Question 83
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 12 hours. How should you design the architecture?
- 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.
- 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.
- 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.
- 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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コメント(13)
- 正解だと思う選択肢: B
  B "Predictive maintenance: asynchronously predicting whether a particular machine part will fail in the next N minutes, given the averages of the sensor's data in the past 30 minutes." https://cloud.google.com/architecture/minimizing-predictive-serving-latency-in-machine-learning#offline_batch_prediction
  
  👍 3
  hiromi2022/12/18
- 正解だと思う選択肢: B
  Answer B I though a lot, since we don't need a real-time response in this scenario, but other options have this problems: A - Http request for sensors data is not a good idea C - What's the point of use Cloud Sql to store the results? D - No BQ mentioned, so why use bq SDK to move data?
  
  👍 2
  LearnSodas2022/12/11
- 正解だと思う選択肢: B
  B is most likely . if you search asynchronous on this page. it appears in the question wants to focus on online prediction with asynchronous mode. https://cloud.google.com/architecture/minimizing-predictive-serving-latency-in-machine-learning#online_real-time_prediction and the question is the same as what has been explained in this section obviously. it is as below. Predictive maintenance: asynchronously predicting whether a particular machine part will fail in the next N minutes, given the averages of the sensor's data in the past 30 minutes.
  
  afte that, you can take a closer look at figure3 and read what it try to describle
  
  C and D it is the offline solution but you opt to use different tools. https://cloud.google.com/architecture/minimizing-predictive-serving-latency-in-machine-learning#offline_batch_prediction
  
  👍 2
  John_Pongthorn2023/01/26
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