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350-401 Practice Question: A data center architect is designing a…
A data center architect is designing a virtualized environment for a latency-sensitive application. The application requires dedicated CPU cores and memory to avoid performance degradation. Which hypervisor feature should be configured to meet this requirement?
⚠ Common exam trap
Cisco often tests the distinction between resource allocation features that guarantee performance (CPU pinning and memory reservation) versus features that optimize utilization or storage I/O, leading candidates to mistakenly select shared storage or memory overcommitment when the question explicitly demands dedicated resources.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Enable CPU pinning and memory reservation for the VM.
CPU pinning binds a VM's virtual CPUs to specific physical cores, ensuring dedicated processing resources and preventing CPU contention from other VMs. Memory reservation guarantees that the specified amount of physical memory is always available to the VM, eliminating the risk of memory swapping or ballooning that would introduce latency. Together, these features provide the deterministic performance required for latency-sensitive applications in a virtualized environment.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Enable CPU pinning and memory reservation for the VM.
Why this is correct
CPU pinning binds the VM's vCPUs to specific physical cores, preventing other VMs from scheduling on those pCPUs and eliminating runqueue contention. Memory reservation guarantees that the hypervisor always allocates the VM's required memory from physical RAM, so the VM never suffers from ballooning or swapping. Together these provide deterministic CPU and memory access, directly mitigating the jitter and latency caused by noisy neighbors.
- ✗
Use a shared storage solution to reduce I/O latency.
Why it's wrong here
Shared storage offloads disk I/O but does nothing to address CPU or memory contention, which is the source of the reported latency. If the problem is compute-bound or memory-bound, the storage subsystem is not the bottleneck, so improving storage throughput is irrelevant. Thus, while shared storage can help with I/O-heavy workloads, it cannot guarantee dedicated CPU resources or reserve memory for the VM.
- ✗
Configure the VM with a large vNUMA node to spread memory access.
Why it's wrong here
A large vNUMA node intentionally spans memory across multiple physical NUMA nodes, which increases remote memory access latency compared to a single local NUMA node. This configuration harms performance because the VM's memory accesses can cross the interconnect instead of staying local to the CPU. The correct approach is to align vNUMA topology with the physical NUMA layout to maintain memory locality, not to spread it wider.
- ✗
Enable memory overcommitment to maximize utilization.
Why it's wrong here
Memory overcommitment deliberately oversubscribes physical memory by allowing more vRAM than available RAM, which forces the hypervisor to reclaim pages via ballooning or swapping when demand peaks. This creates memory contention and can cause severe latency spikes as the VM accesses disk-backed pages. It is the exact opposite of memory reservation, which guarantees physical memory, so it does not reduce latency.
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