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.
Backup and Recovery Fundamentals
Core principles and techniques for protecting, storing, and restoring data. Topics include backup models and types such as full, incremental, and differential backups; snapshotting and replication approaches; transaction log management and write ahead logging for databases; point in time recovery and continuous log based recovery; trade offs between Recovery Time Objective and Recovery Point Objective and how business requirements drive those trade offs; backup storage architectures for file systems and databases including local, off site, and geo redundant storage; backup scheduling, retention lifecycle and policy governance including encryption and compliance; and verification and integrity checks to ensure backups are restorable. Candidates should be able to describe step by step restore procedures, validation and testing techniques, common failure scenarios, and decision trade offs when choosing backup approaches for different application types.
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.
Backup Strategy and Operations
Designing, implementing, and operating reliable backup systems that meet business recovery objectives. Topics include deciding what to back up and why, data classification and tiering, mapping Recovery Time Objective and Recovery Point Objective to backup types and cadence, backup frequency and windows to minimize production impact, retention policy design and lifecycle management, storage selection from local fast restores to off site or cloud tiered storage and air gapped copies, deduplication and compression trade offs, application aware backups and quiescing, automation and orchestration of backup workflows, monitoring and alerting for backup health, integrity verification and routine restore drills, encryption and access controls for backup data, cost and capacity planning, and planning restores at scale. Candidates should also be prepared to discuss trade offs, operational runbooks, and how backup operations support compliance requirements.
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.
Capacity Planning and Forecasting
Covers forecasting demand and planning infrastructure and platform capacity to meet expected business needs reliably and cost effectively. Candidates should be able to analyze historical usage and growth trends, build and validate capacity models, define capacity metrics and thresholds, estimate headroom and safety margins, and translate business growth scenarios into procurement or cloud provisioning plans and timelines. Includes storage and compute lifecycle planning such as archiving and retention strategies, upgrade and rollout planning to avoid disruption, and trade offs between overprovisioning and right sizing. Also addresses design for scale and redundancy, autoscaling and elasticity patterns, load balancing and failover planning, capacity testing and stress testing, monitoring and alerting for capacity signals, and techniques to measure and improve forecast accuracy. Finally it covers operational governance and decision making including cross team resource allocation, capacity reviews, cost optimization and budgeting, runbooks and change control, and alignment of capacity plans with service level objectives and business projections.
Backup and Recovery Administration
Practical administration of backup and recovery tools and processes used to protect business data. Topics include configuring backup policies, schedules, retention rules and storage targets; monitoring job status and configuring alerts for failures; troubleshooting failed backups and performing restores; understanding backup catalogs and media management; capacity planning for backup storage; and explaining backup strategies such as full, incremental, differential, and incremental forever. Candidates may be asked to describe tooling they have used, failure scenarios they resolved, and how they validated recovery objectives.