NCP-AIO Troubleshooting and Optimization Practice Question
What is the most accurate way to verify that a training job is utilizing Tensor Cores?
⚠ Common exam trap
Candidates often assume that high GPU utilization automatically implies Tensor Core usage. They fail to distinguish between general CUDA core compute and specialized matrix operations performed by Tensor Cores.
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
✓
Monitor the SM Tensor utilization metric via Nsight Compute.
Tensor Cores are specialized hardware units for mixed-precision matrix operations. Monitoring the SM occupancy and specific instruction usage via Nsight Compute is the definitive way to confirm their engagement. Understanding how to verify this is essential for engineers to ensure that their optimization efforts, such as mixed-precision training, are actually resulting in the intended hardware utilization and performance improvements.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Check the GPU power consumption in nvidia-smi.
Why it's wrong here
Power consumption is a general metric that indicates the electrical load on the GPU. It does not provide information about which specific execution units, such as Tensor Cores versus CUDA Cores, are being utilized during the execution of a specific deep learning model.
- ✓
Monitor the SM Tensor utilization metric via Nsight Compute.
Why this is correct
Nsight Compute provides specific hardware counters for SM Tensor utilization. This allows an engineer to directly verify if the kernels are issuing the specific matrix multiply-accumulate instructions that run on Tensor Cores, confirming that the hardware acceleration is active for the workload.
- ✗
Check the memory bandwidth in DCGM.
Why it's wrong here
Memory bandwidth is a metric for data movement speed. It does not correlate with the usage of specific compute units like Tensor Cores. A high-bandwidth workload could be running entirely on standard CUDA cores, meaning memory metrics cannot be used to verify Tensor Core usage.
- ✗
Verify the CUDA driver version is above 500.0.
Why it's wrong here
Driver versioning is a requirement for hardware support, but it does not tell you if your specific application is actually making use of the feature. Simply having a new driver does not guarantee that the kernels are written or optimized to utilize the Tensor Cores.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official NVIDIA exam blueprint
This NCP-AIO practice question is part of Courseiva's free NVIDIA 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 NCP-AIO exam.