✅ Data Science Interview Questions with Answers Part-10

• 91. What is model deployment?
Model deployment is the process of making a trained model available for real-world use. This usually involves integrating the model into an application, API, or data pipeline so it can generate predictions on new data reliably and at scale.

• 92. What is batch vs real-time prediction?
Batch prediction processes data in large chunks at scheduled intervals, such as daily or weekly scoring jobs. Real-time prediction generates outputs instantly when a request is made, often through an API. Batch is simpler and cost-effective, while real-time is used when immediate decisions are required.

• 93. What is model drift?
Model drift occurs when the statistical properties of input data or the relationship between inputs and target change over time. This leads to degraded model performance because the model is no longer aligned with current data patterns.

• 94. How do you monitor model performance?
Model performance is monitored by tracking prediction metrics over time, comparing them with baseline values, and checking data distributions for drift. Alerts, dashboards, and periodic evaluations are used to detect issues early and trigger retraining when needed.

• 95. What is feature store?
A feature store is a centralized system that manages, stores, and serves features consistently for training and inference. It ensures the same feature definitions are reused across models, reducing data leakage and duplication.

• 96. What is experiment tracking?
Experiment tracking records details of model experiments such as parameters, metrics, datasets, and code versions. It helps compare experiments, reproduce results, and select the best-performing models systematically.

• 97. How do you explain model predictions?
Model predictions are explained using feature importance, partial dependence plots, or local explanation methods. The goal is to show which features influenced a decision and why, especially for stakeholders and regulatory requirements.

• 98. What is data versioning?
Data versioning tracks changes in datasets over time. It ensures reproducibility by allowing teams to know exactly which data version was used for training, testing, and deployment.

• 99. How do you handle failed models?
Failed models are analyzed to identify root causes such as data drift, poor features, or incorrect assumptions. You may roll back to a previous model, retrain with updated data, or redesign the approach. Failure is treated as feedback, not an endpoint.

• 100. How do you communicate results to non-technical stakeholders?
Results are communicated by focusing on business impact rather than technical details. Visuals, simple language, and clear recommendations are used to explain what changed, why it matters, and what action should be taken.

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