Exam DP-100 topic 3 question 49 discussion

The correct answer should be Yes. I have tested that the code works.

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

This question is out-of-date, obsoleted.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

YES - Chat GPT Yes, the solution meets the goal. The provided code correctly creates a dataset named training_data and loads the data from all files into a single DataFrame. 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 code registers the Azure Blob container datastore named data_store in the workspace. The paths variable is defined to specify the paths of all files to be included in the dataset. 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 executing this code, the dataset training_data will be created, and the data from all files will be loaded into a single DataFrame for further processing.

On exam 18-06-22

A. Yes. from azureml.core import Dataset # Get the default datastore default_ds = ws.get_default_datastore() #Create a tabular dataset from the path on the datastore (this may take a short while) tab_data_set = Dataset.Tabular.from_delimited_files(path=(default_ds, 'diabetes-data/*.csv')) # Display the first 20 rows as a Pandas dataframe tab_data_set.take(20).to_pandas_dataframe()

Read the question again. The answer is not yes because it has missing values in it. The data has to be processed first then it should be put in for the training.

It is yes, no doubt

Hi All. The code works but answer should be yes. Pay attention to the output. Print out the the dataset and you will notice the difference in soruce

The answer is Yes. from azureml.core import Dataset # Get the default datastore default_ds = ws.get_default_datastore() #Create a tabular dataset from the path on the datastore (this may take a short while) tab_data_set = Dataset.Tabular.from_delimited_files(path=(default_ds, 'diabetes-data/*.csv')) # Display the first 20 rows as a Pandas dataframe tab_data_set.take(20).to_pandas_dataframe()

answer is yes. tested

I think the problem is the * for the directory. Something like this: paths = [(data_store, 'data/2018/*.csv'),(data_store, 'data/2019/*.csv')] it would be the correct way

from azureml.core import Workspace, Datastore, Dataset datastore_name = 'your datastore name' # get existing workspace workspace = Workspace.from_config() # retrieve an existing datastore in the workspace by name datastore = Datastore.get(workspace, datastore_name) # create a TabularDataset from 3 file paths in datastore datastore_paths = [(datastore, 'weather/2018/11.csv'), (datastore, 'weather/2018/12.csv'), (datastore, 'weather/2019/*.csv')] weather_ds = Dataset.Tabular.from_delimited_files(path=datastore_paths)