NVIDIA NCA-AIIO Exam Questions

GPU Utilization Across Jobs is the most critical metric for identifying resource contention in a multi-tenant cluster. It shows how GPU resources are divided among workloads, revealing overuse or starvation via tools like nvidia-smi. Option B (temperature) indicates thermal issues, not contention. Option C (network latency) affects distributed tasks. Option D (memory bandwidth) is secondary. NVIDIA's DCGM supports this metric for contention analysis.

Inefficient data loading from storage (B) is the most likely cause of slow performance when storage I/O is consistently high. In AI training, GPUs require a steady stream of data to remain utilized. If storage I/O becomes a bottleneck---due to slow disk reads, poor data pipeline design, or insufficient prefetching---GPUs idle while waiting for data, slowing the training process. This is common in shared clusters where multiple jobs compete for I/O bandwidth. NVIDIA's Data Loading Library (DALI) is recommended to optimize this process by offloading data preparation to GPUs.

Incorrect CUDA version(A) might cause compatibility issues but wouldn't directly tie to high storage I/O.

Overcommitted CPU resources(C) could slow preprocessing, but high storage I/O points to disk bottlenecks, not CPU.

Insufficient GPU memory(D) would cause crashes or out-of-memory errors, not I/O-related slowdowns.

NVIDIA emphasizes efficient data pipelines for GPU utilization (B).

Variability in the GPU load due to other tenants on the same physical hardware is the most likely cause of inconsistent inference times in a cloud-based NVIDIA GPU deployment. In multi-tenant cloud environments (e.g., AWS, Azure with NVIDIA GPUs), instances share physical hardware, and contention for GPU resources can lead to performance variability, as noted in NVIDIA's 'AI Infrastructure for Enterprise' and cloud provider documentation. This affects inference latencydespite identical instance types.

CUDA version differences (A) are unlikely with consistent instance types. Unsuitable model architecture (B) would cause consistent, not variable, slowdowns. Network latency (C) impacts data transfer, not inference on the same instance. NVIDIA's cloud deployment guidelines point to multi-tenancy as a common issue.

A large, high-dimensional healthcare dataset requires techniques to uncover patterns and reduce complexity. K-means Clustering (Option D) groups similar patient records (e.g., by symptoms or outcomes), identifying actionable patterns using NVIDIA RAPIDS cuML for GPU acceleration. Dimensionality Reduction (Option E), like PCA, reduces variables to key components, simplifying analysis while preserving insights, also accelerated by RAPIDS on NVIDIA GPUs (e.g., DGX systems).

SMOTE (Option A) addresses class imbalance, not general pattern extraction. Data Augmentation (Option B) enhances training data, not insight extraction. Batch Normalization (Option C) is a training technique, not an analysis tool. NVIDIA's data science tools prioritize clustering and dimensionality reduction for such tasks.

In a virtualized cloud environment with NVIDIA GPUs, underperformance in one VM despite identical GPU allocation suggests a configuration issue. Misconfigured GPU passthrough settings---where the GPU isn't directly accessible to the VM due to improper hypervisor setup (e.g., PCIe passthrough in KVM or VMware)---is the most likely cause. NVIDIA's vGPU or passthrough documentation stresses correct configuration for full GPU performance; errors here limit the VM's access to GPU resources, causing slowdowns.

Inadequate storage I/O (Option B) or CPU allocation (Option C) could affect performance but would likely impact all VMs similarly if uniform. An incorrect GPU driver (Option D) might cause failures, not just underperformance, and is less likely in a managed cloud. Passthrough misalignment is a common NVIDIA virtualization issue.