Responsible AI practices in MLOps - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Bias Mitigation in AI Models

Which of the following is the most effective method to reduce bias in a machine learning model during training?

A Increasing the number of training epochs without changing data

B Collecting a diverse and representative dataset before training

C Using a more complex model architecture without data changes

D Applying dropout layers to prevent overfitting

💡 Hint

Think about the source of bias and how data affects model fairness.

Bias often comes from unrepresentative data. Collecting diverse data helps the model learn fairly across groups.

💻 Command Output

intermediate

Output of AI Model Explainability Tool

Given the command below to generate feature importance for a trained model, what is the expected output format?

mlflow explain --model-uri runs:/12345/model --explainer shap

A An error indicating missing model URI

B A plain text log showing training accuracy

C A CSV file listing model hyperparameters

D A JSON file with SHAP values for each feature per prediction

💡 Hint

Explainability tools usually output data explaining feature impact.

The SHAP explainer outputs detailed JSON data showing how each feature influences predictions.

🔀 Workflow

advanced

Responsible AI Model Deployment Workflow

Which step should be included before deploying an AI model to production to ensure responsible AI practices?

A Conducting a fairness audit and bias testing on the model

B Increasing batch size during training

C Skipping validation to speed up deployment

D Deploying the model immediately after training completion

💡 Hint

Think about checks that ensure ethical use before release.

Fairness audits help detect and mitigate bias before the model impacts users.

❓ Troubleshoot

advanced

Diagnosing Data Drift Impact on Model Performance

After deploying an AI model, you notice a sudden drop in accuracy. Which command helps detect if data drift is causing this issue?

A mlflow run train.py --no-validation

B mlflow models serve --no-drift-check

C mlflow data drift detect --baseline-data baseline.csv --current-data current.csv

D mlflow experiments delete --all

💡 Hint

Data drift detection compares old and new data distributions.

The 'mlflow data drift detect' command compares baseline and current data to identify drift.

✅ Best Practice

expert

Implementing Privacy-Preserving AI Techniques

Which approach best aligns with responsible AI principles to protect user data privacy during model training?

A Using federated learning to train models without centralizing raw data

B Collecting all user data in a central database for faster training

C Sharing raw training data with third-party vendors for model improvement

D Disabling encryption to speed up data processing

💡 Hint

Consider methods that keep data local and secure.

Federated learning trains models locally on devices, preserving privacy by not sharing raw data.