What is MLOps - Practice Questions & Exercises

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the core purpose of MLOps

What is the main goal of MLOps in a machine learning project?

A To automate and streamline the deployment and management of machine learning models in production

B To create machine learning models without any human intervention

C To replace data scientists with automated tools

D To only focus on data collection and ignore model deployment

💡 Hint

Think about how software development practices apply to machine learning models.

MLOps aims to make deploying and managing machine learning models easier and more reliable, similar to DevOps for software.

💻 Command Output

intermediate

MLOps pipeline step output

You run a step in an MLOps pipeline that trains a model and outputs the model file path. What is the expected output format?

MLOps

train_model --data data.csv --output model.pkl

A {'data': 'data.csv'}

B model.pkl

C Error: missing output path

D {'model_path': 'model.pkl'}

💡 Hint

Outputs are usually structured to be used by next steps.

The output is typically a dictionary or JSON object indicating the location of the saved model file for downstream steps.

🔀 Workflow

advanced

Order of MLOps pipeline stages

Arrange the following MLOps pipeline stages in the correct order from start to finish.

A 3,2,1,4

B 2,3,1,4

C 2,1,3,4

D 1,2,3,4

💡 Hint

Think about what comes first: data, then training, then deployment, then monitoring.

The pipeline starts with collecting data, then training the model, deploying it, and finally monitoring its performance.

❓ Troubleshoot

advanced

Troubleshooting model deployment failure

An MLOps deployment step fails with the error: 'Model file not found'. What is the most likely cause?

A The training step did not save the model file to the expected location

B The data collection step failed to download data

C The monitoring system is offline

D The deployment server has no internet connection

💡 Hint

Check if the model file exists where deployment expects it.

If the model file is missing, deployment cannot proceed. This usually means the training step did not save it correctly.

✅ Best Practice

expert

Best practice for versioning in MLOps

Which practice best ensures reproducibility and traceability of machine learning models in MLOps?

A Manually save model files with timestamps in filenames

B Only version control the model code, ignore data and model files

C Use a version control system to track code, data, and model versions together

D Deploy models without tracking versions to speed up delivery

💡 Hint

Think about how software projects keep track of changes.

Tracking code, data, and models together in version control helps reproduce results and understand changes over time.