Your team is working for a medical research center which researches skin diseases. The Center is in connection with several medical centers where images of actual cases are taken and collected. The image files are sent to your team weekly, and your task is ingesting them into machine learning algorithms. You are using Azure ML Designer to build ML pipelines. 
Which method should you use to ingest data?

Answer options:

A.Create a file dataset from your data folder and drag the dataset to the canvas
B.Drag the Import Data module to the canvas and set it to “File” type and link it to the source data folder
C.Create a tabular dataset from your data folder and drag the dataset to the canvas
D.Create a tabular dataset from your data folder, set the column headers to “All files have the same headers” and drag the dataset to the canvas

Answer: A
Option A is CORRECT because if your data is contained in multiple unstructured (not table-like) files, the recommended way to ingest it in the pipeline is registering the source folder as a dataset, and use the dataset like any other modules in your pipeline. Type of the dataset must be set to “File”.
Option B is incorrect because the Iport Data module can cope with tabular data (CSV files, typically). It is not suitable for unstructured sources, hence “File” type cannot be set.
Option C is incorrect because “Tabular” setting is not applicable for scenarios with unstructured sources. Use “Tabular” dataset type instead.
Option D is incorrect because “Tabular” setting is not applicable for scenarios with unstructured sources. Use “Tabular” dataset type instead. Setting the column headers doesn’t help.
Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/import-data#how-to-configure-import-data
https://docs.microsoft.com/en-us/azure/machine-learning/concept-data#datasets