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Cloud Platforms

AWS, Azure, GCP, serverless, managed services

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More in Cloud Platforms — page 7

Cloud Platforms84 sec read

What a machine image is and why it matters

WHAT IT TESTS: understanding of immutable, reproducible infrastructure. OUTLINE: an image is a frozen template of OS, software, and config; it makes launches identical and fast. RED FLAG: confusing an image with a running instance or a live backup snapshot.

Cloud Platforms85 sec read

Launching a virtual machine in the cloud

WHAT IT TESTS: foundational fluency with cloud compute. OUTLINE: pick image, instance type, network placement and security, attached storage, and access keys. RED FLAG: leaving the instance open to the world or ignoring the security group and key pair.

Cloud Platforms75 sec read

Hybrid cloud bursting from a VMware footprint

WHAT IT TESTS: hybrid architecture and networking depth. OUTLINE: evaluate VMware Cloud on AWS, Azure Arc, Outposts; address connectivity, identity, and data gravity.

Cloud Platforms80 sec read

Designing for portability across two clouds

WHAT IT TESTS: portability versus lock-in judgment. OUTLINE: abstract via containers, Terraform, and open standards; avoid proprietary managed services. RED FLAG: chasing portability for its own sake while ignoring the velocity and cost penalty it imposes.

Cloud Platforms2 min read

When hybrid cloud beats public or private

WHAT IT TESTS: justifying hybrid with a concrete case. OUTLINE: give a scenario like regulated data plus bursty compute; keep sensitive data and legacy systems on-prem, run scalable or customer-facing workloads in public cloud.

Cloud Platforms2 min read

Scalability vs elasticity in the cloud

WHAT IT TESTS: a precise distinction often blurred. OUTLINE: scalability is the ability to handle more load by adding capacity; elasticity is automatically adding AND removing capacity in real time to match demand.

Cloud Platforms2 min read

IaaS vs PaaS for first cloud migration

WHAT IT TESTS: matching a service model to team maturity. OUTLINE: IaaS (lift-and-shift) gives control with high ops overhead; PaaS lowers ops but may need refactoring; for a monolith with limited expertise, IaaS lift-and-shift is the lower-risk first step.

Cloud Platforms2 min read

CapEx vs OpEx in cloud migration

WHAT IT TESTS: the financial shift cloud enables. OUTLINE: CapEx is large upfront asset spend, OpEx is ongoing pay-as-you-go cost; cloud shifts spending from CapEx to OpEx, trading ownership for flexibility.

Cloud Platforms2 min read

Publish/Subscribe Pattern

Publish/subscribe decouples senders from receivers: publishers emit messages to a topic without knowing who consumes them, and subscribers receive messages from topics they care about.

Cloud Platforms2 min read

Persistent Volume and Persistent Volume Claim

Kubernetes separates storage provisioning from consumption. A PersistentVolume is a cluster storage resource an admin or driver provisions; a PersistentVolumeClaim is a pod's request for storage.

How does shared responsibility shift between IaaS and SaaS?
Cloud Platforms2 min read

How does shared responsibility shift between IaaS and SaaS?

Tests your understanding of security ownership across cloud stacks. Strong answer: in IaaS you own OS, apps, and network controls; in SaaS you only own data, identities, endpoints, and accounts while the provider manages the rest.

Cloud Platforms2 min read

Explain the difference between IaaS, PaaS, and SaaS with examples

This tests your grasp of cloud abstraction layers and shared responsibility. A strong answer maps IaaS to raw infrastructure like EC2, PaaS to managed runtimes like Heroku, and SaaS to end-user apps like Gmail.

Cloud Platforms2 min read

Kappa Architecture Unifies Batch and Stream

Kappa treats batch as a special case of streaming: store every event in an immutable log, compute views in real time, and replay the log when logic changes. It removes Lambda's dual codebase but forces you to pay for infinite retention and replay costs.

AWS Cost and Usage Report (CUR)
Cloud Platforms2 min read

AWS Cost and Usage Report (CUR)

Think of CUR as your AWS itemized receipt, delivered daily to S3. It breaks down charges by hour, product, resource, and tag for spreadsheets or Athena queries. Mid-month numbers are estimates, so do not lock budgets until the report finalizes after invoicing.

Cloud Platforms2 min read

CloudEvents: The Event Data Standard

CloudEvents provides a common envelope for event data, enabling routing without custom parsers. Use it when events cross clouds, SaaS tools, or internal services. It standardizes wrappers, not payloads, so producers and consumers still need aligned schemas.

Cloud Platforms2 min read

AWS SDK: Code That Operates Your Cloud

The AWS SDK turns AWS API calls into native code so your app can command S3 or DynamoDB directly. It handles auth, retries, and formatting automatically. The footgun: forgetting region or credentials causes silent failures that look like network errors.

Cloud Platforms2 min read

IAM: The Gatekeeper Framework

IAM is the framework that decides who gets through the door and what they can touch inside your systems. It governs every employee transition, from onboarding to departure. The trap is treating IAM as a one-time setup rather than an ongoing lifecycle process.

Cloud Platforms2 min read

Cloud Disaster Recovery: Planning for Failure

Cloud DR isn't about backups; it's a business continuity plan defining acceptable downtime (RTO) and data loss (RPO). It's for when a whole region fails or a bad deploy corrupts data. The footgun is having a plan but never testing it, creating false security.

Cloud Operating Model: Your Org's Playbook for the Cloud
Cloud Platforms2 min read

Cloud Operating Model: Your Org's Playbook for the Cloud

A Cloud Operating Model is your org's playbook for balancing new cloud capabilities with existing systems. It's used to manage the transition to the cloud with minimal disruption. The footgun is assuming your old processes will work unmodified.

Cloud Platforms89 sec read

ML Model Registry: A Version Control System for Models

A model registry is like Git for machine learning models: a central hub to version, store, and manage them. It's crucial for MLOps, letting teams track which version is in production, compare performance, and roll back. The footgun is using it as just storage.