Technical Tools and Stack Proficiency Questions

Assessment of a candidates practical proficiency across the technology stack and tools relevant to their role. This includes the ability to list and explain hands on experience with programming languages, frameworks, libraries, cloud platforms, data and machine learning tooling, analytics and visualization tools, and design and prototyping software. Candidates should demonstrate depth not just familiarity by describing specific problems they solved with each tool, trade offs between alternatives, integration points, deployment and operational considerations, and examples of end to end workflows. The description covers developer and data scientist stacks such as Python and C plus plus, machine learning frameworks like TensorFlow and PyTorch, cloud providers such as Amazon Web Services, Google Cloud Platform and Microsoft Azure, as well as design tools and research tools such as Figma and Adobe Creative Suite. Interviewers may probe for evidence of hands on tasks, configuration and troubleshooting, performance or cost trade offs, versioning and collaboration practices, and how the candidate keeps skills current.

EasyTechnical

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Describe the difference between a Docker image and a Docker container. Explain how image layers work, give a one-line example Dockerfile instruction that creates a new layer, and list three practical ways to reduce Docker image size for production deployments.

MediumTechnical

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Propose an automated strategy for dependency upgrades in a large codebase. Include tools for automation (e.g., Dependabot), testing and canary deployments, grouping of upgrades, and safeguards for transitive or breaking changes.

HardSystem Design

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Design an end-to-end deployment architecture for a global web application on Kubernetes across three AWS regions that must maintain 99.99% uptime. Include cross-region load balancing, database replication strategy, deployment pipelines, failover, consistency trade-offs, and cost considerations.

MediumTechnical

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Compare Terraform and CloudFormation for infrastructure provisioning in a multi-team organization. Discuss state management, modularity, testing, drift detection, and how they impact collaboration across teams working on shared cloud resources.

HardTechnical

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Design a deployment and monitoring strategy for machine learning models (TensorFlow/PyTorch) supporting canary rollouts, A/B testing, model explainability, retraining triggers, and an automated rollback mechanism when model performance degrades in production.

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