VCP-DCV vSphere Performance and Scaling Practice Question
A vSphere administrator manages a cluster for a VDI workload using VMware Horizon. Each virtual desktop runs a GPU-intensive application and is assigned a vGPU profile (profile: grid_m60-1q) with 4 vCPUs and 8 GB RAM. The ESXi hosts are equipped with NVIDIA M60 GPUs (each host has 2 GPUs, each with 2 physical GPUs? Actually M60 has 2 GPUs on one card, but let's keep generic). The administrator receives complaints of poor graphics performance and high latency. The administrator runs esxtop and observes that the total CPU utilization for the hosts is low (average 30%), but the GPU memory utilization is consistently above 95%, and the vGPU scheduler reports high 'GPU mem' wait times. The number of VMs per host is within the GPU profile limits. What is the most effective way to improve performance?
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
✓
Upgrade the hosts to support GPUs with larger memory capacity or add additional GPUs to each host.
The bottleneck is GPU memory, not CPU. The most effective solution is to upgrade to GPUs with higher memory capacity or add more GPUs. Option D directly addresses this. Option A would increase GPU memory per VM but reduce the total number of VMs, possibly not needed. Option B might not help. Option C could reduce GPU load but at the cost of user experience.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Change the vGPU profile to a larger profile (e.g., grid_m60-2q) for all VMs.
Why it's wrong here
This would allocate more GPU memory per VM, but may exceed GPU capacity and is not the best first step.
- ✗
Increase the CPU reservation for each VDI VM.
Why it's wrong here
CPU resources are not the bottleneck; GPU memory is.
- ✗
Reduce the number of vCPUs per VM from 4 to 2.
Why it's wrong here
Reducing vCPUs may affect application performance and does not address GPU memory contention.
- ✓
Upgrade the hosts to support GPUs with larger memory capacity or add additional GPUs to each host.
Why this is correct
Increasing GPU memory capacity directly resolves the memory contention bottleneck.
Go deeper
Related to this question
About these practice questions
This VCP-DCV question is part of Courseiva's 498-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This VCP-DCV practice question is part of Courseiva's free VMware certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the VCP-DCV exam.