This set builds vocabulary for deploying and configuring apps on a managed application platform.
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At standup, a dev describes a simplified PaaS offering that deploys a containerized or buildpack-detected app directly from a git repository with minimal configuration. Which type of platform fits?
A managed application platform like DigitalOcean App Platform detects a project's build requirements, often via buildpacks, and deploys it directly from a connected git repository, abstracting away manual server provisioning similar to other PaaS offerings. This simplicity trades some low-level infrastructure control for a much faster path from repository to running service. It suits smaller teams or projects that don't need fine-grained infrastructure customization.
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During a design review, the team wants the platform to automatically detect the language and framework of their repository and choose an appropriate build process. Which capability supports this?
Buildpack auto-detection inspects a repository to determine its language and framework, then applies an appropriate predefined build process automatically, sparing the team from writing a custom build configuration for common project types. This convenience is a defining feature of many modern PaaS offerings. A project with unusual requirements can still fall back to a custom Dockerfile when needed.
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In a code review, a dev configures separate compute components within one app spec: a web service, a background worker, and a static site. What does this configuration represent?
Defining separate component types, like a web service, background worker, and static site, within one application specification lets related pieces of an app be deployed and scaled independently while still being managed together as one logical application. This separation mirrors how a real application often has distinct workloads with different resource and scaling needs. Declaring them together in one spec keeps the whole app's configuration version-controlled in one place.
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An incident report shows an app's resource limits were left at default values, causing a memory-intensive job to be killed unexpectedly. What practice would prevent this?
Explicitly sizing resource limits to match a component's actual memory and CPU needs, rather than leaving a one-size-fits-all default in place, prevents a memory-intensive job from being killed unexpectedly when it exceeds an under-provisioned default. This tuning is a standard operational step once a component's real-world resource usage becomes clear. It applies broadly across any managed platform with configurable per-component resource limits.
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During a PR review, a teammate asks how a managed application platform differs from manually provisioning and configuring a droplet or virtual machine. What is the key distinction?
Manually provisioning a droplet requires configuring the OS, runtime, and deployment process by hand, while a managed application platform automates build detection and deployment directly from a git push, trading fine-grained control for reduced operational overhead. This tradeoff is similar across most PaaS-style offerings regardless of vendor. The right choice depends on how much infrastructure control a given project actually needs.