DP-100 · Question #144
DP-100 Question #144: Real Exam Question with Answer & Explanation
The correct answer is B: inputs = [training_ds.as_named_input('training_ds')]. 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_targ
Question
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?
Options
- Aenvironment_definition = {"training_data":training_ds}
- Binputs = [training_ds.as_named_input('training_ds')]
- Cscript_params = {"--training_ds":training_ds}
- Dsource_directory = training_ds
Explanation
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 conda_packages=['pandas','ipykernel','matplotlib'], pip_packages=['azureml-sdk','argparse','pyarrow'], entry_script='diabetes_training.py') https://notebooks.azure.com/GraemeMalcolm/projects/azureml- primers/html/04%20-%20Optimizing%20Model%20Training.ipynb
Topics
Community Discussion
No community discussion yet for this question.