Mastercard Interview Questions (5+ Questions)

Debug Kubernetes Ingress TLS issues: fix certificate secrets, correct NGINX configuration, and restore secure HTTPS access. Hands-on K8s troubleshooting.

Kubernetes ConfigMap Hot-Reloading: api Deployment dev namespace. Fix broken configuration injection by correcting Volume Mount strategies. Enable dynamic configuration updates by resolving sub-path mounting constraints that block file propagation. Critical for decoupling config from code, managing dynamic application properties, and ensuring seamless updates propagate to Pods without restarts.

Kubernetes CronJob Automated Cleanup: ttl-cron maintenance namespace. Schedule high-frequency tasks (* * * * *) and implement automatic resource reclamation using the TTL Controller. Configure the Job template to strictly limit the lifespan of completed execution history. Essential for self-cleaning batch workflows, preventing API server storage bloat, and maintaining long-running cluster hygiene without manual garbage collection.

Clean a text dataset by removing various leading and trailing special characters and whitespace from strings using Python string methods.

We will merge customer and purchase data using Pandas. Pandas is a library that was specifically designed for data analysis and manipulation. We are given two CSV files that are called customers and purchases. We are required to combine both of the initial files using left join and save the result to a new combined data CSV file. The first thing to do is that we import Pandas library and alias it as pd. In order to work with the files, we first have to read them into data frames. Merging Pandas connects two data frames based on a common column or index. It works the same way as JOIN in SQL. Left join returns all rows from the left data frame and the matched rows from the right data frame. If there is no match, then all those fields will contain null values. We use the merge method to connect customers to purchases. On indicates a column that was common for both of the data frames. We set index to false, which prevents Pandas from adding an extra row number column to the output.