Data versioning (DVC) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to initialize a new DVC project in your current directory.

ML Python

dvc [1]

Drag options to blanks, or click blank then click option'

Ainit

Bcreate

Cstart

Dsetup

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'start' or 'create' instead of 'init' will cause an error.

Trying to run 'dvc setup' is not a valid DVC command.

The dvc init command initializes a new DVC project by creating necessary files and folders.

2fill in blank

medium

Complete the code to add a data file named 'data.csv' to DVC tracking.

ML Python

dvc [1] data.csv

Drag options to blanks, or click blank then click option'

Apush

Btrack

Cadd

Dcommit

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'track' is not a valid DVC command.

Confusing 'add' with 'push' which uploads data to remote storage.

The dvc add command tells DVC to track the specified data file or directory.

3fill in blank

hard

Fix the error in the command to push data to remote storage using DVC.

ML Python

dvc [1]

Drag options to blanks, or click blank then click option'

Aupload

Bpush

Ctransfer

Dsend

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'upload' or 'send' causes a command not found error.

Confusing 'push' with 'pull' which downloads data.

The correct command to upload tracked data files to remote storage is dvc push.

4fill in blank

hard

Fill both blanks to create a DVC pipeline stage that runs 'train.py' and saves output to 'model.pkl'.

ML Python

dvc run -n train_model -d train.py -o [1] python [2]

Drag options to blanks, or click blank then click option'

Amodel.pkl

Btrain.py

Attempts:

3 left

💡 Hint

Common Mistakes

Swapping output and script names causes the pipeline to fail.

Using the script name as output or vice versa is incorrect.

The -o option specifies the output file model.pkl, and the command runs python train.py.

5fill in blank

hard

Fill all three blanks to create a DVC pipeline stage that depends on 'prepare.py' and 'data.csv', outputs 'features.csv', and runs 'prepare.py'.

ML Python

dvc run -n prepare_data -d [1] -d [2] -o [3] python prepare.py

Drag options to blanks, or click blank then click option'

Aprepare.py

Bdata.csv

Cfeatures.csv

Dmodel.pkl

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong files as dependencies or output causes pipeline errors.

Mixing up output and dependency files.

The stage depends on the script prepare.py and data data.csv, outputs features.csv, and runs the script.