nerdexam
Microsoft

DP-100 · Question #140

You train and register a model in your Azure Machine Learning workspace. You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline…

The correct answer is A. run(mini_batch) D. init(). A ParallelRunStep batch inferencing script requires exactly two functions: init() and run(mini_batch). init() is called once per worker when the parallel run starts-it is used to load the model into memory. run(mini_batch) is called repeatedly with each mini-batch of input data…

Train and deploy models

Question

You train and register a model in your Azure Machine Learning workspace. You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data. You need to create the inferencing script for the ParallelRunStep pipeline step. Which two functions should you include? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

Options

  • Arun(mini_batch)
  • Bmain()
  • Cbatch()
  • Dinit()
  • Escore(mini_batch)

How the community answered

(26 responses)
  • A
    92% (24)
  • B
    4% (1)
  • C
    4% (1)

Explanation

A ParallelRunStep batch inferencing script requires exactly two functions: init() and run(mini_batch). init() is called once per worker when the parallel run starts-it is used to load the model into memory. run(mini_batch) is called repeatedly with each mini-batch of input data and must return the predictions. The function main() is not part of the ParallelRunStep API. batch() does not exist as a required function. score(mini_batch) follows the real-time inferencing (scoring script) convention, not the batch ParallelRunStep convention.

Topics

#Azure Machine Learning#Batch Inferencing#ParallelRunStep#Model Deployment

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