Short, honest write-ups of the ideas that come up in interviews and on the job. Each one teaches the concept properly, cites the Microsoft documentation, and points back to the class that drills it until it sticks.
Twenty role-level questions grouped by what they actually test — identity, networking, compute, IaC, incident thinking — with what a strong answer contains, not scripts to memorize. Interviewers probe the second follow-up.
Microsoft's platform for building, deploying, and operating AI apps and agents on Azure. What it is versus Azure OpenAI and Azure ML, serverless versus managed-compute deployment, the model catalog and Agent Service, and the…
Azure GPU VMs by family: NC-series for inference and smaller training, ND-series (like ND H100 v5) for large distributed training over InfiniBand, NV-series for visualization. How GPUs, interconnect, quotas, spot capacity, and…
AI infrastructure engineering is cloud engineering pointed at GPU and ML workloads. The honest path from a cloud background: the layer you add on top of Azure fundamentals, the one project that proves it, and why this is one of…
A practitioner's map between the three clouds — subscription vs account vs project, resource group vs folder, RBAC vs IAM, VNet vs VPC, Blob vs S3 — and the handful of places the 1:1 mapping quietly breaks.
All three are billing boundaries — but used at completely different scales, and a GCP project behaves more like an Azure resource group than a subscription. Here's the honest mapping and where it breaks.
The top of the org tree on each cloud — a place to push policy and billing down by inheritance. Same intent, but they leak in three spots: identity binding, the enforcement model, and nesting defaults.
An Azure resource group is a hard container — everything lives in exactly one. A GCP project does the same job. AWS has no native container at all, so you group with tags and stacks. Here's the leak that bites both directions.
Azure splits identity from authorization; AWS IAM fuses both and hands you JSON policy; GCP binds roles to members on a resource. Same three letters, three genuinely different architectures — the biggest place the analogy leaks.
All three are private software-defined networks you carve into subnets — but the regional model is genuinely different: Azure regional, AWS subnets pinned per-AZ, GCP global. Here's where that difference bites.
Same idea on all three — cheap, durable object storage over HTTPS — but Azure adds a storage-account layer that S3 and GCS don't have. Here's the mapping, the tier chart, and the one structural gotcha.
Each cloud's native IaC service — but GCP's is effectively deprecated and Terraform ate all three. Here's the honest mapping and what to actually learn if you're crossing clouds.
All three run the same upstream Kubernetes, so your manifests, kubectl, and Helm charts move unchanged. What differs is the wrapper — control-plane cost, node management, and cluster identity.
Same idea — upload code, pick a trigger, the platform scales it — but the mental models differ. Function Apps, bindings, and hosting plans are the Azure-specific parts that trip people crossing over.
What a live Azure DevOps bootcamp really covers — CI/CD with GitHub Actions, OIDC, GitOps, Bicep and Terraform, AKS, DevSecOps — and how to tell a real cohort with an instructor from a video dump you'll never finish.
How to actually become an Azure cloud engineer — the skills that matter, why self-study stalls in tutorial hell, and how a live cohort with deadlines and a mentor gets you across the line. With Captain O's own no-CS-degree switch as proof.
Why most people who buy a self-paced course stall at week three, what "instructor-led" actually changes, where self-paced genuinely wins — and how to decide honestly by how you learn. A fair comparison, not a pitch.
An honest AZ-900 prep guide — what the exam covers, a realistic 15–30 hour study plan, and the free resources that get you there. Then the part nobody says out loud: passing Fundamentals is the starting line, not the finish.
A plain checklist for picking a live cloud bootcamp that actually gets you hired: live or just recorded, real cloud account or watching, portfolio and repos or a certificate, working engineers or a script — the questions to ask and the red flags to walk from.
The two Expert certs aren't a ladder — they're a fork. AZ-305 (Solutions Architect) designs the estate; AZ-400 (DevOps Engineer) ships it. Which to pick by the role you want, why "which is harder" is the wrong question, and how each relates to AZ-104.
For the content, largely yes — and it's free and official. For AZ-104 specifically, no, not on its own: the exam is hands-on and reading isn't doing. What Microsoft Learn covers, what it misses, and the free stack (free account + free practice assessment) that fills the gap.
Framed as a versus, but it isn't one — you use both. Bash for shell glue and short automation, Python for anything with logic, APIs or data. Which to learn first, when to reach for each, and the rule of thumb for where one ends and the other begins.
The exam that punishes cramming hardest — scenario-heavy, and it assumes you've built real pipelines. What it tests, why build-and-release pipelines are roughly half the marks, a study plan built on one real pipeline, and why the badge only lands the job when a pipeline sits behind it.
Half this debate is a category mistake. Terraform builds the infrastructure; Ansible configures it. What each actually does, why the pros use both, and which one to learn first for a cloud or DevOps job.
"Need experience to get the job, a job to get experience" — false in cloud. A free account, a few real projects, and one public repo turn "no experience" into "here's what I built." What to build, how to keep it free, and how it becomes the answer to "walk me through something you built."
The question that stops good people before they start. You don't need to be a software engineer — but you do need to script. What "coding for DevOps" actually is (Bash, Python, YAML — automation, not apps), what you can skip, and how much is enough.
Most people learn DevOps tools in hype order — Kubernetes first, Linux never — and wonder why they feel behind. The dependency-ordered path instead: foundations first, orchestration late, and why one real pipeline beats a checklist of forty logos.
This comparison no longer has two sides: AZ-204 retired on 31 July 2026 and the Azure Developer Associate retired with it. AZ-104 is the live exam for the administrator and cloud engineer route, AZ-400 for DevOps. What to sit instead, and what to do if you already hold AZ-204.
Both are Microsoft's CI/CD and both ship to Azure with no stored secrets. One is where your portfolio lives; the other is entrenched in the enterprise. Which to learn first for a job — and why "Azure DevOps is dead" is the wrong thing to say in an interview.
AZ-104 proves you can operate Azure by hand; AZ-400 asks you to automate its delivery. That's a change of discipline, not just difficulty — and AZ-400 sits on AZ-104. What changes, the four moves that bridge it, and why DevOps hiring screens for a pipeline you can show, not a badge.
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