Information System Development and Operation
Core concepts
Development models: waterfall (sequential, rigid, clear requirements) vs agile (iterative, incremental, adaptive to change). Understand prototyping, and the purpose of requirements definition, design, implementation, and testing. Know test levels: unit, integration, system, and acceptance testing, and that testing verifies the system meets requirements.
Operations: system lifecycle includes planning, development, operation, and maintenance. Understand the difference between corrective, adaptive, and perfective maintenance. Know why SLAs (service level agreements) define availability and response targets, and what a help desk and incident management do. Understand backup strategies (full vs incremental) and disaster recovery concepts like RTO (recovery time objective) and RPO (recovery point objective).
Traps
Agile is not "no planning"-it plans iteratively. Incremental backup is faster but restoring requires the last full backup plus increments. RTO is about time to restore; RPO is about how much data loss is acceptable-questions often swap them.
Worked example
A system must be back within 4 hours (RTO = 4h) and can lose at most 1 hour of data (RPO = 1h); backups must run at least hourly to meet the RPO.
Rule of thumb
Waterfall fits stable, well-defined requirements; agile fits evolving ones. Match backup frequency to RPO.
New Technologies and New Methods (AI, Big Data, IoT, Agile)
Core concepts
AI: understand machine learning as learning patterns from data, and the difference between supervised learning (labeled data), unsupervised learning (finding structure), and reinforcement learning (learning from rewards). Know that AI quality depends on training data quality and that bias in data produces biased results. Recognize generative AI as producing new content from learned patterns.
Big data: characterized by volume, variety, and velocity (often plus veracity). Understand that big data requires distributed processing and that data analysis supports decision-making.
IoT: sensors and devices connected to networks collecting and exchanging data; consider security risks of many connected devices.
New methods: agile development, DevOps (integrating development and operations), and cloud computing (IaaS, PaaS, SaaS-infrastructure, platform, or software delivered as a service).
Traps
Questions mix up IaaS/PaaS/SaaS: SaaS delivers an application; IaaS delivers virtual infrastructure. Supervised learning requires labeled data; unsupervised does not. Big data is not just "a lot of data"-variety and velocity matter too.
Worked example
A company uses a cloud vendor's email application: that is SaaS. It rents virtual servers and installs its own OS: that is IaaS.
Rule of thumb
Name who manages what: SaaS (vendor manages app), PaaS (vendor manages platform), IaaS (vendor manages infrastructure).