Easy everything in DevOps & Cloud, page 8
Explain the difference between declarative and imperative IaC.
Contrast Terraform with Bash or Ansible; note drift detection, idempotency, and state trade-offs.
How should you manage sensitive data in a committed pipeline file?
Tests that committed pipeline YAML must never store secrets. Answer: fetch at runtime via native secrets manager integrations; if needed, use masked, hidden, protected CI/CD variables; use typed CI/CD inputs for parameters.
Declarative vs scripted pipeline syntax: when to choose each?
This tests Jenkins Pipeline trade-off judgment. Contrast Declarative's opinionated blocks and guardrails with Scripted's raw Groovy flexibility; prefer Declarative for new projects unless complex flow control is needed.
What is Pipeline as Code and its benefits over GUI configuration?
Tests whether you treat delivery pipelines as versioned code. Strong answers define PaC as pipeline definitions in source control, citing branch automation, peer review, audit trails, and single source of truth.
SNAPSHOT and RELEASE versions: differences and appropriate use
SNAPSHOTs are mutable; RELEASEs are immutable and tagged. Use SNAPSHOTs on feature branches and RELEASEs for main.
Difference between unit and integration tests and CI pipeline placement
Unit tests isolate code and run fast in the build stage; integration tests verify real wiring later. Target 70 percent unit tests.
What is a CI/CD quality gate? Give a simple example.
Thresholds blocking merges; a minimal new-code gate with 0 critical issues & 70% new-code coverage.
How do build tools differ from compilers or interpreters?
This tests whether you see compilation as only one step in a repeatable pipeline. A strong answer covers dependency resolution, transitive libraries, task automation, and artifact packaging.
Describe using a pre-push Git hook for checks and its CI limitations.
It tests client-side automation versus server-side policy. An executable .git/hooks/pre-push script runs tests and exits non-zero to block, noting hooks are not cloned, skipped via --no-verify, and local-only. A red flag is treating them as policy gate.
Difference between git merge and git rebase before a pull request
Rebase rewrites SHAs, duplicating CI builds; merge keeps identity and triggers one build.
Describe the typical CI pipeline sequence from push to deploy
This tests stage ordering and failure handling. A strong answer lists build, test, and deploy stages; notes intra-stage parallelism and inter-stage sequencing; and mentions early termination on failure.
What is CI, and what is its single most important goal?
Frequent merges to a shared branch with automated build and test; top goal is a workable integrated codebase.
APM: Turning System Metrics into Business Meaning
APM turns system metrics into business meaning by tracking software performance and availability. It matters most when slowdowns threaten service levels. The footgun is gathering data without translating IT metrics into what the business actually cares about.
SRE Office Hours: Scaling Expertise Through Consultation
SRE Office Hours are a scheduled time for development teams to get expert advice from SREs, scaling reliability knowledge without embedding them everywhere. Use them to review designs or define SLOs. The footgun is treating them as on-call support.
The SRE Mandate: Breaking the Dev vs. Ops Cycle
SRE breaks the classic conflict between developers who want to ship fast and operations teams who want stability. It treats operations as a software problem, not a human one. The footgun is creating a rebranded sysadmin team that still does manual work.
Steady State Hypothesis: The Core of Chaos Engineering
The steady state hypothesis is the core of chaos engineering: you bet your system's key metrics won't change when you break something. It's used to test resilience by defining "normal" (e.g., latency <200ms) and then trying to disrupt it with faults.
Chaos Engineering: Finding Weaknesses Before They Find You
Chaos Engineering intentionally breaks parts of your system to find weaknesses before they cause real outages. It's used to test resilience against events like server crashes or network failures.
Horizontal Scaling: Add More Machines, Not Bigger Ones
Horizontal scaling (scaling out) means handling more load by adding more machines to your resource pool, not upgrading a single one. It’s used for web servers behind a load balancer. The footgun is that your app must be stateless to avoid losing user data.
Single Point of Failure: Your System's Achilles' Heel
A single point of failure (SPOF) is your system's Achilles' heel—one component whose failure causes a total outage. This is critical in high-availability design, like ensuring a website survives a server crash. The footgun is missing implicit SPOFs.
Non-Functional Requirements: How a System Should Be, Not Just What It Does
Non-Functional Requirements (NFRs) define *how well* a system performs its tasks, not *what* tasks it performs. They're the adjectives (fast, secure, reliable) for a system's verbs.
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