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.

0 questions

Capacity Planning and Resource Optimization

Covers forecasting, provisioning, and operating compute, memory, storage, and network resources efficiently to meet demand and service level objectives. Key skills include monitoring resource utilization metrics such as central processing unit usage, memory consumption, storage input and output and network throughput; analyzing historical trends and workload patterns to predict future demand; and planning capacity additions, safety margins, and buffer sizing. Candidates should understand vertical versus horizontal scaling, autoscaling policy design and cooldowns, right sizing instances or containers, workload placement and isolation, load balancing algorithms, and use of spot or preemptible capacity for interruptible workloads. Practical topics include storage planning and archival strategies, database memory tuning and buffer sizing, batching and off peak processing, model compression and inference optimization for machine learning workloads, alerts and dashboards, stress and validation testing of planned changes, and methods to measure that capacity decisions meet both performance and cost objectives.

41 questions

Cloud Platform Fundamentals

Comprehensive understanding of core public cloud services and the primary trade offs when selecting among them across major providers such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform. Candidates should know compute options including virtual machines, managed compute, containers and serverless functions; storage types including object, block and file storage and lifecycle and archival strategies; managed database offerings for relational, non relational, and data warehouse workloads; networking fundamentals including virtual private networks, subnets, routing, load balancing, content delivery networks, and private connectivity; messaging and integration services such as message queues and event streaming; identity and access management and secrets management; monitoring, logging, and observability; autoscaling, elasticity, high availability, and basic disaster recovery patterns; and cost and pricing considerations. The topic also covers the trade offs between managed services and self managed infrastructure in terms of consistency, latency, cost, operational overhead, and durability, and the ability to map common workload requirements to the right service categories.

0 questions

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.

0 questions

Server Infrastructure and Resource Allocation

Covers designing and operating server infrastructure to support applications and workloads reliably and cost effectively. Topics include server architecture and configuration choices such as memory optimized, central processing unit optimized, storage optimized, and general purpose servers and when to use each. Includes virtualization concepts and virtual machines, hypervisors, containerization technologies such as Docker, and orchestration basics such as Kubernetes at a conceptual level. Covers infrastructure provisioning and automation practices including infrastructure as code and configuration management, and how to provision physical servers, virtual machines, or cloud instances. Emphasizes resource allocation and utilization optimization through right sizing, capacity planning, monitoring, scaling strategies, load balancing, redundancy, and high availability. Also addresses network connectivity and bandwidth planning, security and access considerations, cost trade offs, and physical constraints such as power, cooling, and space when comparing bare metal, virtualized, and cloud deployments.

0 questions

Containerization and Virtualization Trade Offs

Examines trade offs between containers and virtual machines and the complexity of orchestrated environments. Topics include hypervisor and virtual machine basics, container isolation and resource models, performance and overhead comparisons, security and attack surface differences, when to prefer virtual machines versus containers, single container versus orchestrated multi container setups, operational complexity versus benefits, and criteria for selecting the appropriate platform at different scales.

0 questions

Amazon Web Services Core Services

Comprehensive knowledge of the foundational Amazon Web Services that are commonly used to design, deploy, and operate cloud applications. This includes compute services such as Amazon Elastic Compute Cloud for virtual machines and instance families, Amazon Web Services Lambda for serverless functions, and Amazon Elastic Beanstalk for managed application platforms; storage services such as Amazon Simple Storage Service for object storage, Amazon Elastic Block Store for block volumes, and Amazon Elastic File System for shared file storage; database services such as Amazon Relational Database Service for managed relational databases, Amazon DynamoDB for NoSQL, and Amazon ElastiCache for in memory caching; networking and content delivery including Amazon Virtual Private Cloud networking concepts, subnets, security groups, load balancers, and Amazon CloudFront; container and orchestration options such as Amazon Elastic Container Service and Amazon Elastic Kubernetes Service; and management and security services including Identity and Access Management, Amazon CloudWatch monitoring and logging, Auto Scaling, and cost and service limit considerations. Candidates should understand core service characteristics, common configuration choices and trade offs, operational considerations such as high availability and fault tolerance, basic security and compliance approaches, performance and cost optimization, and guidance for selecting one service over another for typical application patterns.

0 questions

Azure Compute Options and Trade Offs

Explain compute choices on Azure and the trade offs among them. Cover virtual machines for full operating system control, App Service for managed web hosting, Azure Functions for event driven serverless workloads, container instances for single container tasks, and Azure Kubernetes Service for orchestrated container platforms. For each option describe the operational responsibilities, scalability characteristics, cost model, deployment complexity, and suitability for stateful versus stateless workloads. Be prepared to justify a choice based on latency and performance needs, team expertise, deployment frequency, and cost constraints.

0 questions

Kubernetes Troubleshooting

Covers diagnosing and resolving failures in container orchestration with an emphasis on Kubernetes pod and deployment issues. Topics include Kubernetes architecture and components, pod lifecycle and states, common failure modes such as CrashLoopBackOff, ImagePullBackOff, out of memory conditions, resource limit and quota problems, and node or scheduling issues. Practical debugging skills include using command line inspection and control tools, reading and correlating logs and events, using exec into containers, describing resources, checking node and cluster health, and interpreting health checks, liveness and readiness probes. Also includes distinguishing platform or cluster issues from application level faults, analyzing networking and service discovery problems, using monitoring and observability data, understanding rolling updates and deployment strategies, and applying remediation and rollback techniques for reliable recovery.

0 questions

Cloud Infrastructure Knowledge (AWS/GCP/Azure)

Have working knowledge of at least one major cloud platform: common services (EC2/Compute Engine, RDS/Cloud SQL, S3/Cloud Storage, Load Balancers, VPCs, networking), typical failure modes, and how to troubleshoot within that platform. Understand concepts like availability zones, regions, and cross-region failover.

0 questions

Platform Architecture for Organizational Scale

Designing internal platforms and infrastructure to support large engineering organizations and evolving teams. Topics include developer experience and self service platform design, deployment platforms that enable safe frequent releases for hundreds of engineers, platform automation and observability patterns that provide cross service visibility, governance and operational policies, service onboarding and lifecycle, and how to evolve platform capabilities as headcount and service count grows. Candidates should discuss trade offs between centralized platform services and team autonomy, metrics for platform health, and approaches to encourage adoption while minimizing operational friction.

40 questions

GCP Core Services and Architecture Basics

Core Google Cloud Platform services and architecture concepts, including Compute Engine, Kubernetes Engine (GKE), App Engine, Cloud Functions, Cloud Run, Cloud Storage, BigQuery, Pub/Sub, Cloud SQL/Spanner, and IAM. Covers foundational cloud architecture topics such as projects and resource organization, VPC networking (regions, zones, subnets), identity and access management, security considerations, and scalable design patterns for cloud-native applications on GCP.

0 questions

Kubernetes and Container Orchestration Fundamentals

Core knowledge of container orchestration with a focus on Kubernetes fundamentals and everyday operations. Candidates should understand the role of orchestration and why it is used at scale, and be familiar with core Kubernetes objects including pods as the smallest deployable unit, deployments and replica management, replica sets, services for inter pod communication and service discovery, config maps and secrets for configuration and secret management, and the basic lifecycle of containerized applications. Coverage includes deployment and scaling workflows, rolling update and rollout strategies including rollbacks, common deployment patterns such as blue green and canary, and basic health checks and probe concepts. Practical operational skills include common kubectl commands for deploying, inspecting, scaling and debugging applications, reading pod logs and events, describing resources, port forwarding, and diagnosing common failure modes. Candidates should also understand high level trade offs between using managed cloud provider Kubernetes services and operating self managed clusters including operational overhead, upgrades, cost, and reliability considerations. The scope focuses on fundamentals and day to day operations and excludes advanced cluster administration topics such as custom operators, admission controllers, control plane internals, and deep platform level configuration.

0 questions

Managed Services and Self Managed Tradeoffs

Compare managed cloud services with self managed alternatives and explain when to choose each based on scale cost operational burden and required control. Cover operational ownership upgrade and patch cadences security responsibilities portability and vendor lock in risk performance characteristics and debugging and observability differences. Use concrete workload examples such as managed databases managed event services serverless compute and self hosted containers or virtual machines to justify decisions and to surface long term operational and migration implications.

0 questions

Cloud Architecture Trade-Offs and Design Thinking

Reason about architectural trade offs and design choices for cloud native systems. Candidates should articulate frameworks for comparing managed services with self hosted solutions, weigh consistency versus availability requirements, evaluate cost versus performance trade offs, select appropriate region and replication strategies, and consider operational complexity and failure modes. Interviewers expect clear articulation of constraints, metrics used to make decisions, estimation of costs and latencies, and demonstration of iterative design thinking that balances product needs, delivery speed, and long term maintenance.

0 questions

Cloud Service Selection and Tradeoffs

Frameworks and criteria for evaluating cloud platform services and making pragmatic architecture decisions. Coverage includes managed versus self managed offerings, choice of compute such as serverless functions versus containers versus virtual machines, storage class and database type selection, networking and regional availability, security and compliance considerations, operational burden and run book requirements, vendor lock in and portability risks, performance and latency trade offs, and cost modeling and total cost of ownership. Candidates should be able to articulate trade offs, provide examples of making service choices for real world constraints, and explain how selection affects operability and future evolution.

0 questions

Infrastructure Scaling and Capacity Planning

Operational and infrastructure level planning to ensure systems meet current demand and projected growth. Topics include forecasting demand headroom planning and three to five year capacity roadmaps; autoscaling policies and metrics driven scaling using central processing unit memory and custom application metrics; load testing benchmarking and performance validation methodologies; cost modeling and right sizing in cloud environments and trade offs between managed services and self hosted solutions; designing non disruptive upgrade and migration strategies; multi region and availability zone deployment strategies and implications for data placement and latency; instrumentation and observability for capacity metrics; and mapping business growth projections into infrastructure acquisition and scaling decisions. Candidates should demonstrate how to translate requirements into capacity plans and how to validate assumptions with experiments and measurements.

0 questions

Cost Optimization at Scale

Addresses cost conscious design and operational practices for systems operating at large scale and high volume. Candidates should discuss measuring and improving unit economics such as cost per request or cost per customer, multi tier storage strategies and lifecycle management, caching, batching and request consolidation to reduce resource use, data and model compression, optimizing network and input output patterns, and minimizing egress and transfer charges. Senior discussions include product level trade offs, prioritization of cost reductions versus feature velocity, instrumentation and observability for ongoing cost measurement, automation and runbook approaches to enforce cost controls, and organizational practices to continuously identify, quantify, and implement savings without compromising critical service level objectives. The topic emphasizes measurement, benchmarking, risk assessment, and communicating expected savings and operational impacts to stakeholders.

36 questions

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.

0 questions

Technology and Platform Selection

Evaluation and justification of technologies services and platforms used to implement systems across the stack. Candidates should be able to select compute options including virtual machines containers and serverless platforms as well as orchestration and workflow engines messaging systems batch and streaming processing engines object and block storage data warehouses and other data platforms. The topic encompasses comparing managed services and self managed deployments cloud versus on premise hosting and choosing frameworks runtimes and overall stacks based on workload characteristics. Assessment focuses on weighing trade offs across cost operational overhead reliability latency and throughput scaling characteristics vendor lock in development velocity team familiarity and learning curve maturity and community support security and compliance and monitoring and debugging complexity. Candidates should demonstrate how system requirements map to service capabilities justify build versus buy decisions and managed service choices design proof of concept experiments and outline migration and rollout planning while making pragmatic choices that balance performance cost and operational risk.

0 questions

Cloud Platform Experience

Personal account of hands on experience using public cloud providers and the concrete results delivered. Candidates should describe specific services and patterns they used for compute, storage, networking, managed databases, serverless and eventing, and explain their role in architecture decisions, deployments, automation and infrastructure as code practices, continuous integration and continuous delivery pipelines, container orchestration, scaling and performance tuning, monitoring and incident response, and cost management. Interviewees should quantify outcomes when possible with metrics such as latency reduction, cost savings, availability improvements or deployment frequency and note any formal training or certifications. This topic evaluates depth of practical experience, ownership, and the ability to operate and improve cloud systems in production.

0 questions

Cloud Architecture Fundamentals

Fundamental concepts and design patterns for cloud based systems and services. Topics include core service categories such as compute, storage, networking and databases, virtual machines and containers, serverless computing, managed services, and infrastructure as code. Understand deployment and service models including infrastructure as a service, platform as a service, and software as a service. Evaluate architectural patterns including monolithic, microservices, and serverless approaches, and how they influence scalability, availability, reliability, performance, security, and cost. For more senior roles include distributed systems concepts, consistency and partitioning models, trade off analysis, fault isolation, observability and operational practices in cloud native design.

0 questions

Understanding the Company's Infrastructure Context

Research the company's public infrastructure information (engineering blog, tech talks, published case studies, job description). Understand what systems they operate at scale, what problems they likely face, and what your role would contribute to.

0 questions

Infrastructure Implementation and Operations

Hands on design, deployment, and operational management of infrastructure components and services. This includes setting up and configuring load balancers, database replication and high availability, caching layers, networking and network security, service discovery and routing, container deployment and orchestration, monitoring and observability, logging and alerting, backup and disaster recovery strategies, and secrets management in runtime. Candidates should be able to walk through concrete implementations, explain trade offs, demonstrate troubleshooting and performance tuning, and show how infrastructure components integrate to meet availability, scalability, and security requirements.

0 questions

Cloud Monitoring and Troubleshooting

Monitoring, logging, and troubleshooting in cloud environments. Covers cloud provider monitoring services and patterns such as CloudWatch, Azure Monitor, and Google Cloud Monitoring; setting up metrics and alarms; centralized logging and log analysis in cloud contexts; health checks and auto recovery; diagnosing common cloud issues like performance degradation, networking and permissions problems, and resource exhaustion; and best practices for instrumenting cloud native services.

0 questions

Cloud Platforms and Infrastructure

Comprehensive understanding of cloud computing platforms and core infrastructure concepts. Candidates should know service models including Infrastructure as a Service, Platform as a Service, and Software as a Service, and be familiar with major providers such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure. Core technical knowledge includes compute models, storage systems, networking fundamentals such as domain name system and load balancing, virtual private networks and network segmentation, virtualization, containerization for example Docker, orchestration with Kubernetes, serverless architectures, and microservices. Candidates should be able to evaluate trade offs between managed services and self managed solutions with respect to cost, reliability, operational burden, scalability, performance, security, and vendor lock in, and reason about when to choose platform managed services versus building custom infrastructure. The topic also covers system design considerations for high availability and fault tolerance, capacity planning and autoscaling, monitoring and observability, deployment strategies, and operational practices such as infrastructure as code and continuous integration and continuous delivery. This knowledge is critical for backend engineers, site reliability engineers, and DevOps roles and is increasingly relevant across many engineering positions.

0 questions

Vendor Selection and Technology Evaluation

Approach for evaluating vendors, tools, and technology options before committing to them. Topics include defining requirements and success criteria, creating structured evaluation and scoring criteria, running proof of concept or pilot exercises, benchmarking performance and scalability, assessing total cost of ownership and ongoing operational burden, reviewing service level agreements and support/contract terms, examining vendor roadmaps and interoperability with existing systems, and planning for migration paths and vendor lock in mitigation. Applies to any technology or service selection decision (infrastructure and network hardware, cloud platforms, data and software tooling, security products, SaaS vendors, and similar), not just one domain.

0 questions

Infrastructure Fundamentals

Foundational infrastructure and system components that underpin modern application architectures. Topics include relational and non relational database trade offs, application programming interfaces such as representational state transfer and remote procedure call frameworks, caching layers including Redis and Memcached, load balancers and their layer four and layer seven behaviors, message queues and asynchronous processing patterns, and containerization and orchestration technologies such as Docker and Kubernetes. Candidates should understand each component's purpose, how components interact in an end to end data flow, common failure modes and mitigation strategies, and operational concerns including deployment and rollback strategies, health checks, monitoring, logging, metrics and alerting. Important technical trade offs to reason about include latency and throughput implications, scalability patterns, consistency and durability properties, delivery semantics and idempotency, backpressure and retry strategies, dead letter queues, caching patterns and invalidation, and capacity planning and cost considerations. Interview questions typically probe component selection for given requirements, design choices to improve reliability and maintainability, and how these components fit together in real architectures.

0 questions

Azure and Google Cloud Platforms

Knowledge of core cloud platform concepts and the managed services offered by Microsoft Azure and Google Cloud Platform. Candidates should understand compute options including virtual machines, managed container services, and serverless functions; platform as a service offerings; managed relational and non relational databases; object, block, and file storage; and messaging and eventing services. They should know networking fundamentals such as virtual private cloud or virtual networks, subnets, load balancing, peering, routing, and firewall rules, as well as identity and security topics including identity and access management, role based access control, key management, and encryption. Candidates must be able to map and compare common service equivalents across providers, for example Azure Virtual Machines to Google Compute Engine, Azure App Service to Google App Engine, Azure SQL to Cloud SQL, Azure Blob Storage to Cloud Storage, Azure Service Bus to Cloud Pub Sub, Azure Cosmos DB to Cloud Firestore or Bigtable, and Azure Kubernetes Service to Google Kubernetes Engine. The description also covers operational considerations such as region and zone architecture, high availability and fault tolerance, autoscaling, monitoring and logging, cost and billing differences, service limits and quotas, infrastructure as code and deployment tooling, and multi cloud trade offs including latency, data egress costs, compliance, and vendor lock in. Interview questions typically ask candidates to translate architectures and patterns between platforms, justify service choices, and explain migration or interoperability strategies.

0 questions

Cloud Architecture and Design Patterns

Designing and evaluating cloud native architectures and common architecture patterns, with attention to service capabilities, limits, and operational and security implications. Topics include compute scaling models for virtual instances, managed compute, serverless functions and container orchestration; storage choice, object storage classes, lifecycle policies and caching strategies; managed relational database architectures with high availability patterns such as multiple availability zone deployments and read replicas; trade offs of serverless approaches including cold start and invocation limits; different load balancing approaches for application level and network level traffic; networking and identity boundary design including virtual networks, subnetting, routing, security groups and access control patterns; backup, recovery and disaster recovery planning; deployment patterns such as blue green and canary releases; scalability strategies, performance and latency considerations; vendor lock in, portability and total cost of ownership trade offs; and operational practices for monitoring, limits management and incident response. Candidates should be able to translate requirements into architecture decisions, justify trade offs, and design for resilience, scalability, performance, security and cost.

0 questions

Cloud & Infrastructure Topics

Cost Aware Architecture and Design

Capacity Planning and Resource Optimization

Cloud Platform Fundamentals

Google Cloud Platform Deep Dive

Server Infrastructure and Resource Allocation

Containerization and Virtualization Trade Offs

Amazon Web Services Core Services

Azure Compute Options and Trade Offs

Kubernetes Troubleshooting

Cloud Infrastructure Knowledge (AWS/GCP/Azure)

Platform Architecture for Organizational Scale

GCP Core Services and Architecture Basics

Kubernetes and Container Orchestration Fundamentals

Managed Services and Self Managed Tradeoffs

Cloud Architecture Trade-Offs and Design Thinking

Cloud Service Selection and Tradeoffs

Infrastructure Scaling and Capacity Planning

Cost Optimization at Scale

Infrastructure Automation and Provisioning

Technology and Platform Selection

Cloud Platform Experience

Cloud Architecture Fundamentals

Understanding the Company's Infrastructure Context

Infrastructure Implementation and Operations

Cloud Monitoring and Troubleshooting

Cloud Platforms and Infrastructure

Vendor Selection and Technology Evaluation

Infrastructure Fundamentals

Azure and Google Cloud Platforms

Cloud Architecture and Design Patterns