Tracking datasets with DVC in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

💻 Command Output

intermediate

DVC add command output

You run the command dvc add data/raw_images in your project folder. What is the expected output?

MLOps

dvc add data/raw_images

A Warning: data/raw_images is empty, nothing added.

B

Adding data/raw_images to DVC.
Computing checksum...
Adding 'data/raw_images.dvc' file.

C Error: data/raw_images directory not found.

D dvc: command not found

💡 Hint

Think about what happens when you add a directory with files to DVC.

The 'dvc add' command tracks the specified directory or file by computing its checksum and creating a .dvc file to track it. It does not error if the directory exists and has files.

🧠 Conceptual

intermediate

Purpose of .dvc files

What is the main purpose of the .dvc files created when you track datasets with DVC?

A They are temporary cache files created during data processing.

B They store the actual dataset files inside the project folder.

C They are configuration files for DVC remote storage credentials.

D They contain metadata and checksums to track dataset versions without storing data directly.

💡 Hint

Think about how DVC tracks large files without putting them in Git.

DVC uses .dvc files to store metadata like checksums and paths to data, enabling version control without storing large files in Git.

🔀 Workflow

advanced

Correct sequence to track and push dataset with DVC

Arrange the steps in the correct order to track a new dataset folder data/images and push it to remote storage using DVC.

A 1,2,3,4

B 2,1,3,4

C 1,3,2,4

D 3,2,1,4

💡 Hint

Think about adding data, then staging files for Git, committing, then pushing data to remote.

First, you add the data to DVC, then stage the .dvc file and .gitignore for Git, commit the changes, and finally push the data to remote storage.

❓ Troubleshoot

advanced

DVC push error diagnosis

You run dvc push but get the error: ERROR: failed to upload data/images: Access denied to remote storage. What is the most likely cause?

A The .dvc file for data/images is corrupted.

B The dataset folder data/images does not exist locally.

C The remote storage credentials are missing or incorrect.

D Git repository is not initialized.

💡 Hint

Think about what 'Access denied' means when pushing data.

Access denied errors usually mean the remote storage credentials are missing, expired, or incorrect, preventing upload.

✅ Best Practice

expert

Best practice for dataset versioning with DVC

Which practice ensures reliable dataset versioning and collaboration when using DVC in a team?

A Track datasets with DVC, commit .dvc files to Git, and push data to a shared remote storage.

B Commit only the dataset files directly to Git to keep everything in one place.

C Store datasets only on local machines and share via email to avoid remote storage issues.

D Use DVC without Git to simplify version control.

💡 Hint

Think about how to keep data versions consistent and shareable in a team.

The best practice is to track datasets with DVC, commit the small .dvc files to Git, and push large data files to a shared remote storage for collaboration.

Practice

1. What does the dvc add command do when tracking datasets?

easy

A. It deletes the dataset from the local machine.

B. It uploads the dataset directly to GitHub.

C. It converts the dataset into a database format.

D. It creates a pointer file to track the dataset without storing the data in Git.

Solution

1. Step 1: Understand dvc add purpose

   The dvc add command creates a small pointer file that represents the dataset, instead of storing the full data in Git.

2. Step 2: Recognize data management with DVC

   This pointer file allows Git to track dataset versions without handling large files directly.

3. Final Answer:

   It creates a pointer file to track the dataset without storing the data in Git. -> Option D

4. Quick Check:

   dvc add creates pointer file [OK]

Hint: Remember: DVC tracks data with pointer files, not full data [OK]

Common Mistakes:

• Thinking dvc add uploads data to GitHub
• Confusing dvc add with deleting files
• Assuming data is converted or changed format

2. Which of the following is the correct syntax to track a dataset file named data.csv using DVC?

easy

A. dvc track data.csv

B. dvc add data.csv

C. dvc push data.csv

D. dvc commit data.csv

Solution

1. Step 1: Identify the correct DVC command for tracking

   The command to start tracking a dataset file is dvc add followed by the filename.

2. Step 2: Confirm syntax correctness

   Among the options, only dvc add data.csv correctly adds the file to DVC tracking.

3. Final Answer:

   dvc add data.csv -> Option B

4. Quick Check:

   Use dvc add filename to track data [OK]

Hint: Use dvc add to start tracking files [OK]

Common Mistakes:

• Using dvc track which is not a valid command
• Confusing dvc push with adding files
• Trying dvc commit which is a Git command

3. After running dvc add data.csv, what is the expected output or change in the project directory?

medium

A. A new file named data.csv.dvc is created and data.csv remains in the directory.

B. A new file named data.csv.dvc is created and data.csv is removed.

C. The data.csv file is uploaded to GitHub automatically.

D. The data.csv file is converted to a binary format.

Solution

1. Step 1: Understand dvc add effects on files

   Running dvc add creates a pointer file with extension .dvc that tracks the dataset, but does not delete the original data file.

2. Step 2: Confirm directory state after command

   The original data.csv remains, and a new data.csv.dvc file appears to track it.

3. Final Answer:

   A new file named data.csv.dvc is created and data.csv remains in the directory. -> Option A

4. Quick Check:

   dvc add creates pointer file, keeps data [OK]

Hint: Look for .dvc pointer file; data file stays [OK]

Common Mistakes:

• Assuming data file is deleted after dvc add
• Thinking data is uploaded automatically to GitHub
• Believing data file is converted or changed format

4. You ran dvc add dataset.csv but forgot to commit the generated dataset.csv.dvc file to Git. What problem might occur?

medium

A. Git will track the dataset file directly, causing large repository size.

B. DVC will stop tracking the dataset automatically.

C. The dataset pointer file won't be versioned, causing sync issues between code and data.

D. The dataset file will be deleted from the local machine.

Solution

1. Step 1: Understand the role of the pointer file in Git

   The .dvc pointer file must be committed to Git to keep track of dataset versions alongside code.

2. Step 2: Identify consequences of not committing pointer file

   If the pointer file is not committed, Git won't know about dataset changes, causing mismatch between code and data versions.

3. Final Answer:

   The dataset pointer file won't be versioned, causing sync issues between code and data. -> Option C

4. Quick Check:

   Commit pointer files to Git to sync data and code [OK]

Hint: Always commit .dvc files to Git after adding data [OK]

Common Mistakes:

• Assuming DVC stops tracking automatically
• Thinking dataset file is deleted if not committed
• Believing Git tracks large data files directly

5. You have a dataset folder named images/ with many files. You want to track it with DVC and ensure the dataset version is saved and shared with your team. Which sequence of commands is correct?

hard

A. dvc add images/; git add images.dvc; git commit -m 'Track images dataset'; git push; dvc push

B. git add images/; dvc add images/; git commit -m 'Track images dataset'; dvc push

C. dvc add images/; git add images.dvc; git commit -m 'Track images dataset'; git push

D. dvc add images/; git add images.dvc; git commit -m 'Track images dataset'; dvc push

Solution

1. Step 1: Add the dataset folder with DVC

   Use dvc add images/ to create the pointer file images.dvc tracking the folder.

2. Step 2: Commit the pointer file to Git

   Run git add images.dvc and git commit to version control the pointer file.

3. Step 3: Push Git changes and dataset to remote storage

   First push Git commits with git push, then push dataset files to remote storage with dvc push.

4. Final Answer:

   dvc add images/; git add images.dvc; git commit -m 'Track images dataset'; git push; dvc push -> Option A

5. Quick Check:

   Push Git first, then DVC data to share [OK]

Hint: Push Git commits before dvc push to sync data [OK]

Common Mistakes:

• Pushing DVC data before Git commits
• Adding dataset files directly to Git
• Forgetting to push Git commits before dvc push