Infosys CI/CD, Release Engineering & GitOps interview questions

The opener in most DevOps interviews, and the one most people answer as a list of nouns. What earns the follow-up is saying what each stage is allowed to fail for and how long it takes.

Pipeline as code moved the build definition into the repository, where it is reviewed and versioned with the thing it builds. Declarative is the default answer, and knowing when scripted still earns its place is the follow-up.

Jenkins is mostly plugins, they have a dependency graph nobody drew, and the controller is a single point of failure for every team. The answer is a rehearsal against a copy of production, not a maintenance window and hope.

Stages, jobs, steps, and a pool that supplies the machine. The piece that decides most real designs is the agent pool, because Microsoft-hosted agents cannot reach a private network and most enterprise deployments need to.

Compare proxy, hosted and group repositories. Trace a tested artifact into production, and understand when a cache can keep builds running during an upstream outage.

Git merges by comparing both branches against their common ancestor. A conflict means both sides changed the same region, and the resolution nobody checks is the one that compiles and is semantically wrong.

Follow commit identities through a rebase, recover a collaborator’s work with an explicit boundary, and explain the limits of force-with-lease and revert.

Calculate the pipeline critical path, separate runner queues from execution, and improve feedback time without hiding flaky or skipped checks.

Testing is a pipeline design problem in this role, not a QA one. The constraint is the question: you cannot run everything on every commit, so what you choose to defer is the answer.

Follow scheduling eligibility, job isolation and artifact transfer, then diagnose a queued job whose available runners cannot satisfy its requirements.

One Application declaring others sounds like trivia until you need to rebuild a cluster in an afternoon or hand a team a bounded slice of the platform. Here is when the pattern earns its keep, and when ApplicationSet should replace it.

Distinguish executor capacity, placement and provisioning failures, then size an agent pool from measured resource use and cleanup behaviour.

Same commit, different dependency trees: the vocabulary answer is lockfiles, the practitioner answer includes frozen installs, integrity hashes and pinned toolchains.

Staging is a snowflake maintained by accretion. Rank drift sources by risk, shrink them mechanically with parity by construction, and stop calling staging the last line of defence.

Hooks must finish in seconds or developers bypass them, and a bypassed hook protects nothing. Budget feedback latency numerically and split checks by authority and hermeticity.

Central pipeline logic fails two ways: forked copies that drift, or a platform team queue every change must wait behind. Versioned templates with pinned consumers and declared hooks avoid both.

Compare rollout capacity, traffic exposure and state compatibility, then work through a payments canary with too little evidence to promote.

Compare a shared-library patch across forty services, including affected builds, staged releases, runtime inventory and source-access boundaries.

The security argument for GitOps is more specific than 'Git is the source of truth'. Pull deployment can remove cluster credentials from CI, and that single fact is most of the case.

Connect version numbers to consumer promises, then test an additive API change against older clients and distinguish release metadata from artifact identity.

Public fork PRs normally get a read-only token and no repository secrets. Privileged triggers, runner access and untrusted shell input can break that boundary.

Base layers are dependencies: digest-pin them, let bots bump them weekly, and let CVE disclosures trigger scoped rebuilds through the normal pipeline. Days, not quarters.