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Infrastructure Automation and Provisioning Questions

Covers designing, implementing, and operating automated infrastructure provisioning and configuration using Infrastructure as Code practices and complementary automation patterns. Candidates should be able to select and author declarative infrastructure definitions with tools such as Terraform, CloudFormation, and Azure Resource Manager templates, and discuss configuration management tools such as Ansible, Puppet, or Chef. Core skills include modular and reusable code organization for multiple environments, variable and output management, remote state management and locking, idempotency and atomicity of operations, and version control integration for infrastructure artifacts. Candidates should understand testing and validation practices including linting, plan or dry run validation, unit and integration testing of infrastructure changes, and drift detection and remediation. The topic includes strategies for safe changes and rollbacks, change coordination, error handling and recovery, and deployment patterns such as canary and blue green where applicable. It also encompasses automation and orchestration patterns, immutable infrastructure and self healing practices, autoscaling and scaling policies, automated patching and updates, secrets handling patterns using secret managers, and integrating observability and monitoring into automated workflows. Finally, candidates should be able to reason about trade offs between imperative and declarative approaches, scaling Infrastructure as Code across large projects and teams, and security and compliance considerations for automated provisioning.

EasyTechnical
0 practiced
Briefly explain blue-green and canary deployment patterns from an infrastructure perspective and give a concrete infrastructure-level example where each is preferred. Include considerations for traffic routing, resource duplication, cost, and the added complexity around database schema changes during these deployments.
EasyTechnical
0 practiced
Describe the core components and considerations needed to implement autoscaling for a stateless web service in AWS or Azure. Include launch/scale configuration, health checks, metrics to drive scaling decisions, cooldown windows, and SLO/latency trade-offs you would use to set thresholds.
HardTechnical
0 practiced
Design a policy enforcement and remediation system for IaC to block insecure configurations (public S3 buckets, overly permissive IAM). Compare OPA/Gatekeeper, Terraform Sentinel, and Cloud Custodian for placement (pre-commit, CI, runtime), scalability, policy lifecycle management, and how you'd maintain and test a shared policy library across teams.
MediumTechnical
0 practiced
A customer reports production instances have drifted from the desired Terraform state because of several manual emergency hotfixes. Propose a step-by-step remediation and prevention plan that minimizes downtime: detection, impact assessment, reconciliation path, communication with stakeholders, and automation to prevent recurrence.
HardTechnical
0 practiced
For large Terraform stacks that take long to apply and occasionally timeout, propose concrete techniques to speed up runs and improve reliability. Discuss state-splitting (separate stacks), resource targeting, parallelism settings, remote execution options (runners, Terraform Cloud), provider retries/rate-limit handling, and reorganizing monolithic repos into smaller logical units.

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