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Exam DP-100 topic 3 question 64 discussion

Actual exam question from Microsoft's DP-100
Question #: 64
Topic #: 3
[All DP-100 Questions]

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You create a model to forecast weather conditions based on historical data.
You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script.
Solution: Run the following code:

Does the solution meet the goal?

  • A. Yes
  • B. No
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Suggested Answer: B 🗳️
The two steps are present: process_step and train_step
The training data input is not setup correctly.
Note:
Data used in pipeline can be produced by one step and consumed in another step by providing a PipelineData object as an output of one step and an input of one or more subsequent steps.
PipelineData objects are also used when constructing Pipelines to describe step dependencies. To specify that a step requires the output of another step as input, use a PipelineData object in the constructor of both steps.
For example, the pipeline train step depends on the process_step_output output of the pipeline process step: from azureml.pipeline.core import Pipeline, PipelineData from azureml.pipeline.steps import PythonScriptStep datastore = ws.get_default_datastore() process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--data_for_train", process_step_output], inputs=[process_step_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py
by kty at March 18, 2021, 9:03 p.m.

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kty
Highly Voted 2 years, 7 months ago
datastore = ws.get_default_datastore() process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--data_for_train", process_step_output], inputs=[process_step_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])
upvoted 14 times
ML_Novice
1 year, 12 months ago
how the first preprocess step doesn't require any input entry? thanks for your response
upvoted 2 times
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snegnik
5 months, 2 weeks ago
Where is input for process_step?
upvoted 1 times
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dev2dev
Highly Voted 2 years, 7 months ago
only line#2 should be fixed: data_output = PipelineData("processed_data", datastore=datastore)
upvoted 8 times
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snegnik
Most Recent 5 months, 2 weeks ago
I think this should be better datastore = ws.get_default_datastore() input_data = PipelineData("input_data", datastore=datastore) process_step_output = PipelineData("processed_data", datastore=datastore) model_output = PipelineData("trained_model", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", input_data], inputs=[input_data], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--processed_data", process_step_output, "--output_model", model_output], inputs=[process_step_output], outputs=[model_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step])
upvoted 1 times
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skrjha20
2 years, 1 month ago
Code should be as below. datastore = ws.get_default_datastore() process_step_output = PipelineData("processed_data", datastore=datastore) process_step = PythonScriptStep(script_name="process.py", arguments=["--data_for_train", process_step_output], outputs=[process_step_output], compute_target=aml_compute, source_directory=process_directory) train_step = PythonScriptStep(script_name="train.py", arguments=["--data_for_train", process_step_output], inputs=[process_step_output], compute_target=aml_compute, source_directory=train_directory) pipeline = Pipeline(workspace=ws, steps=[process_step, train_step]) However in 2 nd line tried to use read_csv() which is wrong
upvoted 2 times
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SnowCheetah
2 years, 4 months ago
https://docs.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipelinedata?view=azure-ml-py This one is incorrect since process_step_output is set up incorrectly in order to set up whole pipeline you should connect all components ( compute node, datastore) set within workspace because when code is running. The resources will generate within your Azure Machine Learning resource which mean reading data locally will not to be able to read when compute note is spawn.
upvoted 2 times
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