Deployment
130 bites tagged Deployment: interview questions with model answers, and 60-second explainers.
Test yourself: Top 30 Deployment interview questions →Multiple choice, with the correct answer and why it is correct on every question. Free, no sign-in.Graceful shutdown implementation and zero-downtime deployments?
Listen for SIGTERM, stop accepting new connections, drain in-flight requests, close resources, exit.
dependencies vs devDependencies in production?
Dependencies are needed at runtime, devDependencies only for development. npm install includes both, npm ci --only=production excludes dev.
How does Node.js cluster module enable zero-downtime restarts?
Master-worker architecture, graceful shutdown of old workers, routing new requests to new workers.
Limits of OTA Updates and Runtime Versions
OTA ships only JS and assets; native changes need a store build; runtime versions gate compatibility.
CI/CD Pipeline for React Native to TestFlight and Play
A triggered workflow installs deps, builds signed artifacts on macOS and Linux runners, uploads via Fastlane to TestFlight and the Play internal track.
Over-the-Air Updates for JavaScript-Only Fixes
Ship a new JS bundle the app downloads at runtime and applies on next launch, with no store review.
Android App Bundles versus Universal APKs
An AAB is an upload format; Play generates per-device APKs via dynamic delivery, shrinking download size.
Managing Per-Environment Configuration in React Native
Use per-environment env files plus build flavors or schemes, inject at build time, keep secrets off the device.
iOS Provisioning Profiles and Signing Certificates
The certificate proves developer identity, the App ID names the app, the profile binds certificate, App ID, and devices.
Zero-downtime blue-green deploys on Kubernetes
Run blue and green deployments, switch a Service or ingress selector to the new color after readiness probes pass, drain old pods gracefully, and handle backward-compatible DB migrations.
Rolling update vs blue-green deployment
Rolling replaces instances gradually with minimal extra capacity but mixes versions; blue-green runs two full environments for instant switch and rollback at double the cost.
Auto-rollback on failed blue-green cutover
Shift traffic gradually behind a smart router, use deep health checks plus real SLI monitoring, and auto-revert to blue on breach while blue stays warm.
Design automated canary analysis scoring
Track the golden-signal SLIs, compare canary to baseline statistically, weight and combine into a score with promote/rollback thresholds.
Diagnose a degraded canary release
Check statistical significance versus baseline, confirm apples-to-apples comparison, isolate the cause via traces and logs, then weigh the regression against SLO budget.
Blue-green deploys with schema migrations
The shared database means both versions hit one schema, so breaking changes must be split into backward-compatible steps via expand-and-contract.
What is shadow deployment for ML models?
New model receives mirrored live traffic but its predictions are logged, not served; validates real-world behavior and latency without user risk.
Automated rollback for a failed model deploy
Detect failure via health and metric checks, automatically route traffic back to the last known-good version (blue-green or canary), keep the registry entry but unpromote, and alert.
CI/CD for microservice-based ML systems
Independent per-service pipelines, contract testing to protect interfaces and schemas, and incremental deploys (canary, blue-green); manage data and model contracts, not just code.
Staging Environments for ML Pipelines
A staging environment mirrors production so models and pipelines are validated on production-like data and infrastructure before release. It catches drift, integration breaks, and serving regressions early, making promotion to production a safe, repeatable…
PTQ versus QAT for INT8 quantization
PTQ quantizes a trained model with light calibration, fast and cheap but more accuracy loss; QAT simulates quantization during training, higher accuracy but costly.
Model quantization benefits and risks
Quantization stores weights and activations in lower precision like INT8 or INT4; benefits are smaller memory and faster, cheaper inference; risk is accuracy loss.
StatefulSet vs Deployment: what's the difference?
Deployments treat Pods as interchangeable; StatefulSets give stable ordinal names, stable per-Pod storage via volumeClaimTemplates, and ordered rollout.
Upgrading a stateful Flink job without losing state
Take a savepoint, stop with drain, deploy new jar, restore from savepoint with matching operator UIDs.
Roll out a design system color change
Update the token source, rebuild outputs, version and publish, bump the app's dependency, verify contrast and visual regressions.
Get Deployment bites daily.
Five a day, five minutes, offline. With quizzes so it sticks.
The iPhone app is on the way
We are building it. Until it lands, nothing here is held back from you: every interview card, your saved cards, streaks and the job board all work in Safari, plus hundreds of free practice quizzes of thirty questions each. Sign in and it all carries over to the app the day it arrives.
Want it as an icon? Tap Share at the bottom of Safari, then Add to Home Screen. It opens full screen and the cards you have read stay available offline.