Cloud & Infrastructure Topics
Cloud platform services, infrastructure architecture, Infrastructure as Code, environment provisioning, and infrastructure operations. Covers cloud service selection, infrastructure provisioning patterns, container orchestration (Kubernetes), multi-cloud and hybrid architectures, infrastructure cost optimization, and cloud platform operations. For CI/CD pipeline and deployment automation, see DevOps & Release Engineering. For cloud security implementation, see Security Engineering & Operations. For data infrastructure design, see Data Engineering & Analytics Infrastructure.
Load Balancing and Horizontal Scaling
Covers principles and mechanisms for distributing traffic and scaling services horizontally. Includes load balancing algorithms such as round robin, least connections, and consistent hashing; health checks, connection draining, and sticky sessions; and session management strategies for stateless and stateful services. Explains when to scale horizontally versus vertically, capacity planning, and trade offs of each approach. Also includes infrastructure level autoscaling concepts such as auto scaling groups, launch templates, target tracking and step scaling policies, and how load balancers and autoscaling interact to absorb traffic spikes. Reviews different load balancer types and selection criteria, integration with service discovery, and operational concerns for maintaining availability and performance at scale.
Observability and Monitoring Architecture
Designing and architecting end to end observability and monitoring systems that scale, remain reliable under load, and do not become single points of failure. Topics include deciding which telemetry to collect and why including metrics logs traces and events, instrumentation strategies, collection models such as push versus pull, high throughput telemetry ingestion and pipeline design, time series storage and compression, aggregation and partitioning strategies, metric cardinality and retention tradeoffs, distributed tracing propagation and sampling strategies, log aggregation and secure storage, selection of storage backends and time series databases, storage tiering and cost optimization, query and dashboard performance considerations, access control and multi tenancy, integration with deployment pipelines and tooling, and design patterns for self healing telemetry pipelines. Senior level assessments include designing scalable ingestion and aggregation architectures, storage tiering and query performance optimization, cost and operational tradeoffs, and organizational impacts of observability data.
Google Cloud Platform Deep Dive
In depth coverage of Google Cloud Platform services across compute, networking, storage, orchestration, and platform integrations. Areas include Compute Engine instance management and machine type selection, Google Kubernetes Engine concepts for container orchestration, managed databases such as Cloud SQL and Firestore, Cloud Storage features including versioning and lifecycle, networking components including Virtual Private Cloud, VPN and load balancing, content delivery with Cloud CDN, eventing and messaging with Pub/Sub, and analytics with BigQuery. Candidates should demonstrate design decisions, operational practices, scaling strategies, security and identity considerations, and service limits and trade offs for production deployments.
Infrastructure Automation and Provisioning
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.
Google Cloud Platform Familiarity
Evaluates knowledge of Google Cloud Platform offerings and how to apply them when building and operating services. Candidates should be able to explain core compute options, managed container and serverless offerings, storage and database choices, messaging and streaming services, data analytics tools, identity and access management basics, networking and load balancing, monitoring and logging, and how to reason about costs, quotas, and scaling trade offs. Practical examples of deployments, migrations, or service selection decisions are useful to demonstrate applied familiarity.
Cost Aware Architecture and Design
Focuses on how architectural decisions and design patterns affect operating cost and total cost of ownership. Interviewees should be able to reason about trade offs such as managed services versus self managed components, always on virtual machines versus event driven or serverless approaches, reserved versus on demand capacity, use of spot or preemptible instances, and multi region or edge placement. Candidates should demonstrate techniques for reducing cost through storage class selection and lifecycle policies, caching and batching, query and workload optimization, data transfer minimization, and workload isolation. The topic also covers modeling and communicating cost trade offs, estimating ongoing operating expense for alternative designs, and choosing architecture that balances budget constraints with reliability, performance, and engineering effort.