Easy everything in DevOps & Cloud, page 3
What happens to volume data when a Pod is deleted?
EmptyDir is tied to the Pod and erased when the Pod is deleted; a PVC-backed PV with Retain keeps the data after the PVC is released for manual recovery.
What are PersistentVolumes and PersistentVolumeClaims for?
A PV is a cluster storage resource the admin provisions; a PVC is a user's request for size and access mode; Kubernetes binds them, decoupling Pods from storage details.
How Compose services reach each other by name
Services share a default network and the web app uses the database's service name as the hostname; Docker's embedded DNS resolves it to the container IP.
Persist PostgreSQL data across compose down
Define a named volume and mount it at the database's data directory (/var/lib/postgresql/data); named volumes survive compose down.
Start Compose services detached and view one service's logs
Docker compose up -d starts everything detached; docker compose logs -f web follows only the web service's logs.
Dockerfile CMD versus ENTRYPOINT
ENTRYPOINT sets the fixed executable; CMD sets default args or the default command; run-time args override CMD but append to ENTRYPOINT. Use together to make a fixed binary with overridable defaults.
Dockerfile COPY versus ADD
COPY just copies local files; ADD also auto-extracts local tarballs and can fetch remote URLs; prefer COPY for predictability, use ADD for local archive extraction.
Build, tag, and run a container with port mapping
Docker build -t my-app:1.0 . to build and tag; docker run -d -p 8080:80 my-app:1.0 to run detached with host:container port mapping.
What is a container vs a VM?
Containers share the host kernel and isolate via namespaces and cgroups; VMs run a full guest OS on a hypervisor; containers are lighter and faster.
Lift-and-shift versus replatforming migration
Rehosting moves apps unchanged for speed and low risk; replatforming makes targeted optimizations for cloud benefits; explain the speed-versus-value trade-off.
Explain the Well-Architected Framework pillars
Name the pillars, operational excellence, security, reliability, performance efficiency, cost optimization, and sustainability, and explain each briefly.
Feeding large object-store data into training
Stream data instead of copying it all to disk, use streaming/pipe modes, shard and prefetch in parallel, and pack many small images into larger files.
Pre-built AI service vs custom model
Choose a managed service for speed, no ML expertise, and common tasks; build custom for domain-specific needs, control, or cost at scale.
CSV vs JSON vs Parquet for analytics
CSV and JSON are row-based, human-readable, and bulky; columnar Parquet/ORC compress well and read only needed columns; choose columnar for analytics.
ETL versus ELT in cloud data platforms
ETL transforms before loading into the target; ELT loads raw first then transforms in the warehouse, leveraging cheap storage and elastic compute.
Data lake versus data warehouse
Lakes store raw, schema-on-read data of any type cheaply; warehouses store curated, schema-on-write structured data for fast SQL; choose a lake for varied raw data and ML.
On-Demand vs Reserved vs Spot pricing models
On-Demand is flexible but priciest, Reserved trades a 1-3 year commitment for discounts, Spot is cheapest but interruptible.
Attribute cloud costs to teams
Tag resources with team and project metadata, activate them as cost-allocation tags, group the cost report by that tag, and enforce tagging with policy.
Automate patching across a VM fleet
Use a patch or config tool to target by tag, roll out in canaried waves with health checks, and prefer immutable golden images long term.
What is Infrastructure as Code?
IaC defines infra in version-controlled files, giving repeatability, peer review, and drift-free consistency, unlike error-prone console clicks.
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