NVIDIA NCA-AIIO Exam Practice Test Instant Access

Question 1

You have deployed an AI training job on a GPU cluster, but the training time has not decreased as expected after adding more GPUs. Upon further investigation, you observe that the GPU utilization is low, and the CPU utilization is very high. What is the most likely cause of this issue?

AThe AI model is not compatible with multi-GPU training.

BThe GPUs are not properly connected in the cluster.

CIncorrect software version installed on the GPUs.

DThe data preprocessing is being bottlenecked by the CPU.

Answer : D

The data preprocessing being bottlenecked by the CPU is the most likely cause. High CPU utilization and low GPU utilization suggest the GPUs are idle, waiting for data, a common issue when preprocessing (e.g., data loading) is CPU-bound. NVIDIA recommends GPU-accelerated preprocessing (e.g., DALI) to mitigate this. Option A (model incompatibility) would show errors, not low utilization. Option B (connection issues) would disrupt communication, not CPU load. Option C (software version) is less likely without specific errors. NVIDIA's performance guides highlight preprocessing bottlenecks.

Question 2

Your company is planning to deploy a range of AI workloads, including training a large convolutional neural network (CNN) for image classification, running real-time video analytics, and performing batch processing of sensor dat

a. What type of infrastructure should be prioritized to support these diverse AI workloads effectively?

AOn-premise servers with large storage capacity

BCPU-only servers with high memory capacity

CA cloud-based infrastructure with serverless computing options

DA hybrid cloud infrastructure combining on-premise servers and cloud resources

Answer : D

Diverse AI workloads---training CNNs (compute-heavy), real-time video analytics (latency-sensitive), and batch sensor processing (data-intensive)---require flexible, scalable infrastructure. A hybrid cloud infrastructure, combining on-premise NVIDIA GPU servers (e.g., DGX) with cloud resources (e.g., DGX Cloud), provides the best of both: on-premise control for sensitive data or latency-critical tasks and cloud scalability for burst compute or storage needs. NVIDIA's hybrid solutions support this versatility across workload types.

On-premise alone (Option A) lacks scalability. CPU-only servers (Option B) can't handle GPU-accelerated AI efficiently. Serverless cloud (Option C) suits lightweight tasks, not heavy AI workloads. Hybrid cloud is NVIDIA's strategic fit for diverse AI.

Question 3

In an effort to improve energy efficiency in your AI infrastructure using NVIDIA GPUs, you're considering several strategies. Which of the following would most effectively balance energy efficiency with maintaining performance?

AEnabling deep sleep mode on all GPUs during processing times

BDisabling all energy-saving features to ensure maximum performance

CRunning all GPUs at the lowest possible clock speeds

DEmploying NVIDIA GPU Boost technology to dynamically adjust clock speeds

Answer : D

Employing NVIDIA GPU Boost technology to dynamically adjust clock speeds is the most effective strategy to balance energy efficiency and performance in an AI infrastructure. GPU Boost, available on NVIDIA GPUs like A100, adjusts clock speeds and voltage based on workload demands and thermal conditions, optimizing Performance Per Watt. This ensures high performance when needed while reducing power use during lighter loads, as detailed in NVIDIA's 'GPU Boost Documentation' and 'AI Infrastructure for Enterprise.'

Deep sleep mode (A) during processing disrupts performance. Disabling energy-saving features (B) wastes power. Lowest clock speeds (C) sacrifice performance unnecessarily. GPU Boost is NVIDIA's recommended approach for efficiency.

Question 4

You are responsible for managing an AI infrastructure that runs a critical deep learning application. The application experiences intermittent performance drops, especially when processing large datasets. Upon investigation, you find that some of the GPUs are not being fully utilized while others are overloaded, causing the overall system to underperform. What would be the most effective solution to address the uneven GPU utilization and optimize the performance of the deep learning application?

AReduce the size of the datasets being processed.

BIncrease the clock speed of the GPUs.

CAdd more GPUs to the system.

DImplement dynamic load balancing for the GPUs.

Answer : D

Intermittent performance drops due to uneven GPU utilization stem from workload imbalance. Dynamic load balancing, enabled by NVIDIA tools like Triton Inference Server or Kubernetes with GPU Operator, redistributes tasks based on GPU utilization, ensuring even processing of large datasets. This optimizes performance in DGX or multi-GPU setups by preventing overload and underuse, directly addressing the root cause.

Reducing dataset size (Option A) compromises model quality and doesn't fix distribution. Increasing clock speed (Option B) may help overloaded GPUs but not underutilized ones. Adding GPUs (Option C) increases capacity but not balance. NVIDIA's infrastructure solutions favor dynamic balancing for critical applications.

Question 5

You are managing an AI-driven autonomous vehicle project that requires real-time decision-making and rapid processing of large data volumes from sensors like LiDAR, cameras, and radar. The AI models must run on the vehicle's onboard hardware to ensure low latency and high reliability. Which NVIDIA solutions would be most appropriate to use in this scenario? (Select two)

ANVIDIA DGX A100

BNVIDIA Jetson AGX Xavier

CNVIDIA GeForce RTX 3080

DNVIDIA DRIVE AGX Pegasus

ENVIDIA Tesla T4

Answer : B, D

For an autonomous vehicle requiring onboard, low-latency AI processing:

NVIDIA Jetson AGX Xavier(B) is a compact, power-efficient edge AI platform designed for real-time processing in embedded systems like vehicles. It supports sensor fusion (LiDAR, cameras) and deep learning inference with high reliability.

NVIDIA DRIVE AGX Pegasus(D) is a purpose-built automotive AI platform for Level 4/5 autonomy, delivering high-performance computing for sensor data processing and decision-making with automotive-grade reliability.

NVIDIA DGX A100(A) is a data center system, unsuitable for onboard vehicle use due to size and power requirements.

NVIDIA GeForce RTX 3080(C) is a consumer GPU for gaming, lacking automotive certification or edge optimization.

NVIDIA Tesla T4(E) is a data center GPU for inference, not designed for vehicle onboard processing.

NVIDIA's DRIVE and Jetson platforms are tailored for autonomous vehicles (B and D).

Question 6

You are part of a team working on optimizing an AI model that processes video data in real-time. The model is deployed on a system with multiple NVIDIA GPUs, and the inference speed is not meeting the required thresholds. You have been tasked with analyzing the data processing pipeline under the guidance of a senior engineer. Which action would most likely improve the inference speed of the model on the NVIDIA GPUs?

AEnable CUDA Unified Memory for the model.

BDisable GPU power-saving features.

CProfile the data loading process to ensure it's not a bottleneck.

DIncrease the batch size used during inference.

Answer : C

Inference speed in real-time video processing depends not only on GPU computation but also on the efficiency of the entire pipeline, including data loading. If the data loading process (e.g., fetching and preprocessing video frames) is slow, it can starve the GPUs, reducing overall throughput regardless of their computational power. Profiling this process---using tools like NVIDIA Nsight Systems or NVIDIA Data Center GPU Manager (DCGM)---identifies bottlenecks, such as I/O delays or inefficient preprocessing, allowing targeted optimization. NVIDIA's Data Loading Library (DALI) can further accelerate this step by offloading data preparation to GPUs.

CUDA Unified Memory (Option A) simplifies memory management but may not directly address speed if the bottleneck isn't memory-related. Disabling power-saving features (Option B) might boost GPU performance slightly but won't fix pipeline inefficiencies. Increasing batch size (Option D) can improve throughput for some workloads but may increase latency, which is undesirable for real-time applications. Profiling is the most systematic approach, aligning with NVIDIA's performance optimization guidelines.

Question 7

Which of the following best describes a key difference between training and inference architectures in AI deployments?

ATraining requires higher compute power, while inference prioritizes low latency and high throughput.

BInference requires more memory bandwidth than training.

CTraining architectures prioritize energy efficiency, while inference architectures do not.

DInference architectures require distributed training across multiple GPUs.

Answer : A

Training and inference have distinct architectural needs. Training requires higher compute power to process large datasets and update models iteratively, as seen in NVIDIA DGX systems with multi-GPU setups. Inference prioritizes low latency and high throughput for real-time predictions, optimized by NVIDIA TensorRT on GPUs or edge devices like Jetson.

Inference doesn't inherently need more memory bandwidth (Option B)---training often does. Training prioritizes performance over energy efficiency (Option C), unlike inference's focus on both. Inference doesn't require distributed training (Option D)---that's a training trait. NVIDIA's ecosystem reflects Option A's distinction.

NVIDIA AI Infrastructure and Operations NCA-AIIO Exam Practice Test