Exam DP-100 topic 3 question 47 discussion

The Answer is clearly no

Its seems the question has been changed/updated since some of these comments.

This question is out-of-date, obsoleted. Should be from azure.ai.ml import ... not from azureml.core import Dataset Reference: https://github.com/Azure/azure-sdk-for-python/tree/azure-ai-ml_1.11.1/sdk/ml/azure-ai-ml#authenticate-the-client

It meets the requirements. See example below from Microsoft: # create tabular dataset from all csv files in the directory tabular_dataset_3 = Dataset.Tabular.from_delimited_files(path=(datastore,'weather/**/*.csv')) # create tabular dataset from multiple paths data_paths = [(datastore, 'weather/2018/11.csv'), (datastore, 'weather/2018/12.csv')] tabular_dataset_4 = Dataset.Tabular.from_delimited_files(path=data_paths) Link: https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py#azureml-data-dataset-factory-tabulardatasetfactory-from-delimited-files SaM

No, the solution does not meet the goal. The code provided to create the dataset and load the data into a single DataFrame is incorrect. To create a dataset named training_data and load the data from all files into a single DataFrame, you need to modify the code as follows: python Copy code from azureml.core import Dataset paths = [(data_store, 'data/2018/*.csv'), (data_store, 'data/2019/*.csv')] training_data = Dataset.Tabular.from_delimited_files(paths) data_frame = training_data.to_pandas_dataframe() Explanation: The paths variable is updated to specify the paths of all files to be included in the dataset. In this case, it includes all CSV files in the /data/2018 and /data/2019 directories. The Dataset.Tabular.from_delimited_files() method is used to create the dataset training_data by providing the paths variable. The to_pandas_dataframe() method is called on the training_data dataset to load the data from all files into a single pandas DataFrame. By making these changes, the code will create the desired dataset and load the data from all files into a single DataFrame.

Correct answer is Yes Link :: https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py&viewFallbackFrom=azure-ml-pyandhttps%3A%2F%2Flearn.microsoft.com%2Fen-us%2Fpython%2Fapi%2Fazureml-core%2Fazureml.data.tabulardataset%3Fview%3Dazure-ml-py

Question updated as of Jan 2023.... based on https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.data.dataset_factory.tabulardatasetfactory?view=azure-ml-py and https://learn.microsoft.com/en-us/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py Answer seems to be A - YES

yes Answer is B

Answer is correct

web_path ='https://dprepdata.blob.core.windows.net/demo/Titanic.csv' titanic_ds = Dataset.Tabular.from_delimited_files(path=web_path, set_column_types={'Survived': DataType.to_bool()}) # preview the first 3 rows of titanic_ds titanic_ds.take(3).to_pandas_dataframe() https://docs.microsoft.com/en-us/azure/machine-learning/how-to-create-register-datasets#set-data-schema

I think the answer might be 'yes'. see this similar example for parquet files: datastore_path = [(dstore, dset_name + '/*/*/data.parquet')] dataset = Dataset.Tabular.from_parquet_files(path=datastore_path, partition_format = dset_name + '/{partition_time:yyyy/MM}/data.parquet') the partition_format argument appears optional. reference: https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/work-with-data/datasets-tutorial/timeseries-datasets/tabular-timeseries-dataset-filtering.ipynb

answer is yes.