Data pipelines with DVC in MLOps - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

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

MLOps

dvc [1]

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

Acreate

Bstart

Cinit

Dsetup

Attempts:

3 left

💡 Hint

Common Mistakes

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

Trying 'create' or 'setup' are not valid DVC commands.

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

2fill in blank

medium

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

MLOps

dvc [1] data.csv

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

Acommit

Btrack

Cpush

Dadd

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'track' is not a DVC command.

Using 'push' uploads data but does not track it.

Using 'commit' is a Git command, not DVC.

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 run a DVC pipeline stage defined in 'dvc.yaml'.

MLOps

dvc [1]

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

Arun

Bexecute

Cstart

Dbuild

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'execute' or 'build' will cause an error.

Using 'start' is not a valid DVC command.

The correct command to run a pipeline stage in DVC is dvc run.

4fill in blank

hard

Fill both blanks to define a DVC pipeline stage that runs 'python train.py' with input 'data.csv' and output 'model.pkl'.

MLOps

dvc run -n train_model -d data.csv -o [1] [2]

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

Amodel.pkl

Bpython train.py

Ctrain.py

Ddata.csv

Attempts:

3 left

💡 Hint

Common Mistakes

Putting the input file as output.

Using only the script name without 'python'.

The -o flag specifies the output file, here model.pkl. The command to run is python train.py.

5fill in blank

hard

Fill all three blanks to create a DVC pipeline stage that depends on 'prepare.py', uses 'data/raw.csv' as input, and outputs 'data/clean.csv'.

MLOps

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/raw.csv

Cdata/clean.csv

Ddata/input.csv

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong input file names.

Missing the script as a dependency.

The stage depends on the script prepare.py and the raw data data/raw.csv. The output is the cleaned data data/clean.csv.