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About this interview
A technical interview on Testing and CI/CD for ML, pitched at the hard level. A voice AI interviewer leads the conversation, adapts its questions to your answers, keeps you on topic, and afterward gives you honest, specific feedback on where you were strong and where to improve. Expect roughly 30 minutes.
What you'll be assessed on
Distinguish unit, integration, and data validation tests in an ML codebase and explain what each catches
Describe how a CI/CD pipeline for ML extends traditional software CI by adding model training, evaluation gating, and registry promotion steps
Explain how Infrastructure-as-Code tools like Terraform bring reproducibility to ML infrastructure
Discuss code-quality practices specific to ML projects: pre-commit hooks, linting, and makefile-based automation
Topics covered
ML Test TaxonomyUnit TestingCode QualityCI/CD for MLData ValidationIntegration TestingInfrastructure as CodeModel Testing
A few sample questions
Just examples to set expectations - the real interview has many more and adapts to your responses.
“When we talk about testing an ML system, what are the three main levels of tests — unit, integration, and data validation — and what does each one actually check?
“In a GitHub Actions CI workflow, what is the difference between a CI job triggered on a pull request and a CD job triggered on a push to the main branch? Why are these two triggers separate for ML?
“If an ML pipeline's data validation step raises an alert on schema drift in a new data batch, what should happen next in an automated CI/CD system versus what should require a human decision?