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OS · Question Set
Advanced Topics & Case Studies interview questions for placements and exams.
Questions
10
Included in this set
Subject
OS
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Difficulty
Mixed
Level of this set
Go through each question and its explanation. Use this set as a focused practice pack for OS.
Load balancing in multi-processor or multi-server OS architectures aims to spread tasks so that no single node becomes a bottleneck, resources are utilised evenly, system throughput is maximised and performance stays predictable. This is particularly relevant to modern OS in data-centres and cloud settings.
For complete preparation, combine this set with full subject-wise practice for OS. You can also explore other subjects and sets from the links below.
In a monolithic kernel architecture many OS services (file system, network stack, device drivers) run in kernel space, giving high performance but larger trusted code base. A microkernel aims to keep the kernel minimal (basic services such as scheduling, IPC) and move other services to user space. This improves modularity, isolation and reliability but can incur overhead from message passing between services.
When demand-paged processes each keep triggering page faults because their combined working sets exceed available physical memory frames, the system spends more time swapping pages in and out than doing productive work — this is thrashing. Recognising thrashing is a classic advanced OS interview topic. :contentReference[oaicite:0]{index=0}
In a cloud infrastructure environment operating systems evolve to support massively scalable workloads, multi-tenant isolation, live migration, and hardware abstraction. Two important optimisations are: 1) Lightweight virtual machines or containers as first-class citizens rather than full user desktops, reducing overhead and enabling fast deployment. 2) Live migration of VMs or containers between hosts to enable maintenance, load balancing, scaling without downtime. Additionally OS kernels often incorporate I/O vertical scaling (e.g., SR-IOV for network devices), NUMA awareness for large servers, and software-defined storage or network abstractions. Holding awareness of these modern OS trends strengthens interview responses.
In a NUMA architecture each processor has local memory which it can access more quickly; accessing another processor’s memory or a shared region may incur higher latency. Modern OS kernels must be NUMA-aware when scheduling tasks or managing memory to optimise performance on large servers. Recognising this shows advanced OS knowledge. :contentReference[oaicite:1]{index=1}
Observability in modern operating systems and platforms means having visibility into performance, reliability and resource usage at runtime so that operators can detect anomalies, diagnose faults and optimise behaviour. OS-level metrics to track include CPU utilisation, queue lengths of ready and I/O queues, memory usage, page fault rate, context-switch rate, interrupt rate, I/O latency, scheduler load, and for cloud OS also VM/container migration events, network packet drops and NUMA node imbalances. Referencing case studies of production OS helps you show applied knowledge in interviews.
In OS scheduling and design trade-offs often include choosing between faster response time (good for interactive tasks) and higher throughput (good for batch tasks). A policy that favours one may hurt the other. Recognising trade-offs, as well as how real-world systems balance them, is a strong sign in interviews. :contentReference[oaicite:2]{index=2}
A real-time operating system is designed to guarantee that tasks meet timing constraints (deadlines), not just logical correctness. In a hard RTOS missing a deadline is a failure. It typically offers deterministic scheduling, minimal latency, priority inversion protection, and often supports preemption of nearly any code. The trade-offs are that RTOS may sacrifice throughput or flexibility for predictability, may use static memory and simpler APIs, and often run fewer services. For interview answers mention examples (embedded systems, autopilot, medical devices) and contrast with general-purpose OS which target max utilisation, flexibility and feature-richness.
When an OS encounters a kernel panic or fatal error the typical steps are: 1) Immediately halt or freeze further non-critical activities to prevent corruption. 2) Log diagnostic information (stack trace, register state, memory dump) so engineers can analyse root cause. 3) Attempt automatic recovery if supported (e.g., reboot to safe mode or use a redundant node in high availability systems). 4) For critical services the OS or platform might trigger fail-over to a standby system, preserve user sessions or state if possible, and restore service quickly. 5) On restart perform integrity checks (file system check, memory diagnostic) before resuming full operations. Mentioning high-availability OS patterns (redundant kernel, live patching) adds depth.
Live patching is the ability to apply changes (patches) to a running kernel or OS components without rebooting. This is critical for high-availability systems where downtime must be minimised. It typically involves loading new code modules, redirecting function calls, ensuring state consistency, and monitoring for regressions. By enabling hotfixes, security updates or module upgrades without taking the system offline, live patching supports service continuity and maintenance windows. In interview discussion you might mention how OS ensures safe patching (symbol versioning, interrupt quiescence, duplication of code) and risks (memory leaks, inconsistent state).