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Exam DP-100 topic 3 question 43 discussion

Actual exam question from Microsoft's DP-100

Question #: 43
Topic #: 3

You use the Azure Machine Learning service to create a tabular dataset named training_data. You plan to use this dataset in a training script.
You create a variable that references the dataset using the following code: training_ds = workspace.datasets.get("training_data")
You define an estimator to run the script.
You need to set the correct property of the estimator to ensure that your script can access the training_data dataset.
Which property should you set?

A. environment_definition = {"training_data":training_ds}
B. inputs = [training_ds.as_named_input('training_ds')]
C. script_params = {"--training_ds":training_ds}
D. source_directory = training_ds

Show Suggested Answer

Suggested Answer: B 🗳️
Example:
# Get the training dataset
diabetes_ds = ws.datasets.get("Diabetes Dataset")
# Create an estimator that uses the remote compute
hyper_estimator = SKLearn(source_directory=experiment_folder, inputs=[diabetes_ds.as_named_input('diabetes')], # Pass the dataset as an input compute_target = cpu_cluster, conda_packages=['pandas','ipykernel','matplotlib'], pip_packages=['azureml-sdk','argparse','pyarrow'], entry_script='diabetes_training.py')
Reference:
https://notebooks.azure.com/GraemeMalcolm/projects/azureml-primers/html/04%20-%20Optimizing%20Model%20Training.ipynb

by epgd at June 28, 2020, 10:15 p.m.

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ljljljlj

Highly Voted 2 years, 7 months ago

On exam 2021/7/10

upvoted 8 times

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DanielGP

Highly Voted 3 years ago

Train a model from a tabular dataset: Now that you have datasets, you're ready to start training models from them. You can pass datasets to scripts as INPUTS in the estimator being used to run the script.

upvoted 7 times

treadst0ne

2 years, 11 months ago

I concur.

upvoted 2 times

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Peeking

Most Recent 11 months ago

Selected Answer: B

B would seem the closest answer but it was not properly described in the answers: from azureml.core import ScriptRunConfig src = ScriptRunConfig(source_directory=script_folder, script='train_titanic.py', # pass dataset as an input with friendly name 'titanic' arguments=['--input-data', titanic_ds.as_named_input('titanic')], compute_target=compute_target, environment=myenv) # Submit the run configuration for your training run run = experiment.submit(src) run.wait_for_completion(show_output=True) Reference: https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-train-with-datasets

upvoted 1 times

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ning

1 year, 8 months ago

I guess if you want to pass the parameter into estimator it will be C; now the question is asking inside the estimator you are reading the input, it is B ... Still a bit confusion ...

upvoted 2 times

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JJason

2 years, 2 months ago

why should not inputs = [training_ds.as_named_input('training_ds').as_mount()]?

upvoted 3 times

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chaudha4

2 years, 9 months ago

Estimator is deprecated. Can anyone confirm if they saw a question on this topic lately ?

upvoted 5 times

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shivaborusu

3 years, 3 months ago

Answer must be C

upvoted 1 times

shivaborusu

3 years, 3 months ago

Taking back.. B is correct answer

upvoted 8 times

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epgd

3 years, 7 months ago

why the correct answer is not script_params ?

upvoted 1 times

amelia

3 years, 7 months ago

The question does not specify using command-line arguments to pass to the training script, so I guess it is assumed the traiining data is specified as an input compute target in the estimator properties.

upvoted 3 times

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