Model registry in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Purpose of a Model Registry

Which of the following best describes the main purpose of a model registry in machine learning projects?

A To store, version, and manage machine learning models for easy deployment and tracking

B To train machine learning models faster using distributed computing

C To collect raw data from various sources for model training

D To visualize model predictions in real-time dashboards

💡 Hint

Think about how teams keep track of different versions of models and their metadata.

A model registry is a centralized place to store and manage machine learning models, including their versions, metadata, and deployment status. It helps teams track and deploy models efficiently.

❓ Model Choice

intermediate

Choosing Model Version in Registry

You have three versions of a model registered: v1, v2, and v3. v3 has the best accuracy but is unstable in production. Which version should you deploy from the registry?

A Deploy v3 because it has the best accuracy

B Deploy v2 if it balances accuracy and stability

C Deploy v1 because it is the first version

D Deploy none and retrain a new model

💡 Hint

Consider both accuracy and stability for production use.

The best model to deploy balances accuracy and stability. Even if v3 has the highest accuracy, instability can cause failures. Choosing v2 if it is stable and reasonably accurate is best.

❓ Metrics

advanced

Tracking Model Metrics in Registry

Which metric is NOT typically tracked in a model registry to evaluate model performance?

A User interface color scheme

B Model accuracy on validation data

C Training time duration

D Number of model parameters

💡 Hint

Think about metrics related to model quality and resource use.

Model registries track metrics like accuracy, training time, and model size to evaluate and compare models. User interface color scheme is unrelated to model performance.

🔧 Debug

advanced

Identifying Issue in Model Registry Usage

A team notices that after registering a new model version, the deployment still uses the old model. What is the most likely cause?

A The model registry deleted the new model version automatically

B The new model version has a syntax error in the training code

C The training data was not updated before retraining

D The deployment pipeline is not configured to pull the latest model version from the registry

💡 Hint

Think about how deployment pipelines interact with model registries.

If deployment still uses the old model, it usually means the pipeline is not set to fetch or update to the latest registered model version.

❓ Predict Output

expert

Output of Model Registry Version Query Code

What is the output of this Python code snippet querying a model registry?

ML Python

model_versions = ['v1', 'v2', 'v3']
registered_versions = {v: 'deployed' if v != 'v2' else 'staging' for v in model_versions}
print(registered_versions['v2'])

A KeyError

B deployed

C staging

D SyntaxError

💡 Hint

Check the dictionary comprehension logic carefully.

The dictionary comprehension sets 'staging' for version 'v2' and 'deployed' for others. So registered_versions['v2'] returns 'staging'.