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.
Microsoft Product and Azure Fundamentals
Practical knowledge of Microsoft enterprise offerings and core Azure services, and the ability to map those products to customer scenarios. Candidates should understand when to recommend Azure compute database and networking services versus platform services when Microsoft 365 Dynamics 365 or Power Platform are more appropriate and how to articulate trade offs for scalability security compliance and cost. Topics include Azure infrastructure and platform services virtual machines app services managed databases storage networking identity and governance considerations migration patterns and common integration approaches with on premise systems.
Solution Architecture and Custom Configuration
Design custom solution architectures for specific customer scenarios. Given a customer's technical environment, scale, requirements, and constraints, propose how to deploy and configure the company's product. Consider integration points, performance tuning, security configurations, migration strategy, phased rollout approaches, and cost optimization. Show understanding of the customer's technical stack and explain integration approaches. At mid-level, you should be able to sketch basic solution designs without needing engineering consultation for typical scenarios.
Microsoft Products and Ecosystem
Familiarity with Microsoft enterprise products and how they fit together to solve customer problems. Candidates should understand Office 365 and Microsoft 365 offerings, Teams, Dynamics 365, Power Platform, and Azure services and be able to explain common integration patterns, typical enterprise deployment scenarios, identity and access management considerations, licensing and bundling implications, and customer migration and adoption challenges. Interviewers assess the ability to map Microsoft product capabilities to customer business outcomes, articulate when to use platform services versus third party tools, and discuss security and compliance considerations in enterprise environments.
Azure Services Deep Dive
In depth knowledge of Microsoft Azure platform services, architecture trade offs, operational considerations, and best practices. Candidates should be able to discuss compute options such as Azure Virtual Machines sizing, availability sets and zones, maintenance windows, and scale sets; storage options including Azure Storage account types, redundancy and replication choices, and backup strategies; database offerings such as Azure SQL Database, managed instances, high availability and replication patterns; networking fundamentals including Azure Virtual Network design, subnetting, network security groups, peering, service endpoints and private links, and load balancing with Application Gateway and Azure Load Balancer; container orchestration and related considerations such as Azure Kubernetes Service concepts, cluster sizing, upgrades, and networking models; serverless and platform services such as Azure Functions and event driven architectures; infrastructure as code and deployment pipelines using Azure DevOps and templates; identity and secrets management with Azure Active Directory and Azure Key Vault including secret rotation; monitoring, logging, and cost optimization strategies; and security, compliance, and disaster recovery planning across these services. Interview evaluation focuses on architectural reasoning, trade off analysis, operational runbook concerns, scaling and performance tuning, failure modes and recovery, and automation using provider native tooling.
Cloud Cost Modeling and Planning
Building and evaluating cost models for cloud adoption and migration, including estimating total cost of ownership, comparing on premise and cloud deployment costs, and developing business cases for cloud investments. Skills include mapping workload characteristics to cloud pricing models, forecasting usage and growth, accounting for licensing and migration one time costs, modeling ongoing operational costs, and incorporating discounts such as reserved capacity and spot pricing. Also covers scenario and sensitivity analysis, capacity planning and rightsizing, cost governance practices such as tagging and showback or chargeback, measuring financial outcomes and return on investment, and strategies for post migration cost optimization and financial monitoring.
Cloud Migration and Modernization
Covers approaches and planning for moving workloads and data to the cloud and modernizing legacy systems. Candidates should be able to explain migration patterns such as lift and shift, replatforming, and refactoring; how to conduct discovery and assessment; strategies for database and data transfer including bulk migration and change data capture; application modernization options including containerization and managed platform services; cutover planning, rollback and validation techniques; tooling and automation to reduce risk; and how to evaluate cost, risk, governance, security, and return on investment for a phased modernization program.
Legacy System Assessment
Evaluating legacy applications and infrastructure to determine cloud readiness, technical debt, dependencies, compliance and regulatory constraints, and organizational barriers to modernization. Topics include inventorying legacy assets, dependency and integration analysis, risk and cost assessment, migration approaches and trade offs (rehost, refactor, rearchitect, replace), prioritization of migration waves based on business impact and complexity, and planning mitigations for data, compliance, and continuity. Candidates should explain how they identify migration blockers, estimate effort and risk, and develop phased modernization strategies that balance business value and technical feasibility.
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.
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.