Question
You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.
The default datastore of workspace1 contains a folder named sample_data. The folder structure contains the following content:
```text
sample_data
├── MLTable
├── file1.txt
├── file2.txt
└── file3.txt
```
You write Python SDK v2 code to materialize the data from the files in the sample_data folder into a Pandas data frame.
You need to complete the Python SDK v2 code to use the MLTable folder as the materialization blueprint.
How should you complete the code? To answer, select the appropriate options in the answer area.
```python
import mltable
tbl = mltable.[Dropdown 1]("[Dropdown 2]")
df = tbl.to_pandas_dataframe()
```
Explanation
Dropdown 1: mltable.load() reads an existing MLTable blueprint and returns a table object that to_pandas_dataframe() can materialize. save writes a blueprint out rather than reading one, and take is a table method that returns the first rows, not a module function that opens a blueprint.
Dropdown 2: load takes the path of the folder that contains the MLTable file, so the argument is ./sample_data, as in the documented mltable.load("./titanic/") example. Pointing at ./sample_data/MLTable names the file itself instead of its folder, and a glob of the text files bypasses the blueprint entirely.
