NCA Data Center GPU Solutions 4 — Questions and Answers
Question 1: NVIDIA's CUDA architecture uses a hierarchy of threads. Which is the correct ordering from smallest to largest grouping?
- Thread → Block → Grid
- Warp → Thread → Block → Grid
- Block → Thread → Warp → Grid
- Thread → Warp → Block → Grid (Correct answer)
Correct answer: Thread → Warp → Block → Grid
CUDA organizes threads into warps (32 threads), warps into blocks, and blocks into a grid, forming the complete hierarchy for GPU parallel execution.
Question 2: What advantage does NVIDIA's Confidential Computing feature on H100 provide for data center tenants?
- It allows GPU workloads to run faster by skipping authentication overhead
- It protects GPU workloads and data from privileged software attacks using hardware-based Trusted Execution Environments (Correct answer)
- It encrypts NVLink communication between GPUs in the same node
- It provides isolated GPU memory partitions with no performance overhead
Correct answer: It protects GPU workloads and data from privileged software attacks using hardware-based Trusted Execution Environments
H100 Confidential Computing uses hardware TEEs to protect AI workloads and model weights even from the hypervisor, cloud operator, or other privileged system software.
Question 3: Which NVIDIA data center GPU is specifically optimized for inference workloads with a balance of INT8 performance and low power consumption?
- A100 80GB SXM4
- H100 NVL
- L4 (Ada Lovelace) (Correct answer)
- V100 32GB
Correct answer: L4 (Ada Lovelace)
The L4 is based on Ada Lovelace architecture and is designed for energy-efficient inference, video AI, and graphics workloads with a 72W TDP.
Question 4: A company wants to deploy GPU-accelerated Kubernetes workloads. Which NVIDIA component enables GPUs to be scheduled as resources within Kubernetes?
- NVIDIA DCGM Exporter
- NVIDIA GPU Operator (Correct answer)
- CUDA Toolkit only
- NVIDIA TensorRT
Correct answer: NVIDIA GPU Operator
The NVIDIA GPU Operator automates the deployment and management of GPU software components (drivers, CUDA, device plugin) needed for GPU-accelerated Kubernetes workloads.
Question 5: What is the primary function of NVIDIA TensorRT in a data center inference pipeline?
- Distributes inference requests across multiple GPU nodes using load balancing
- Optimizes and compiles trained neural network models for high-throughput, low-latency GPU inference (Correct answer)
- Manages GPU memory allocation across containerized inference services
- Provides a REST API gateway for serving machine learning models
Correct answer: Optimizes and compiles trained neural network models for high-throughput, low-latency GPU inference
TensorRT takes a trained model and applies optimizations like layer fusion, precision calibration (INT8/FP16), and kernel auto-tuning to maximize inference throughput on NVIDIA GPUs.
Question 6: Which metric best describes the peak theoretical AI training performance of an NVIDIA A100 80GB GPU?
- 312 TFLOPS BF16 Tensor Core (Correct answer)
- 1 PFLOPS FP32
- 80 TFLOPS FP64 standard
- 400 TOPS INT4
Correct answer: 312 TFLOPS BF16 Tensor Core
The A100 delivers approximately 312 TFLOPS of BF16 Tensor Core performance, which is the key benchmark for transformer model training throughput.
Question 7: When planning a data center GPU deployment using NVLink Bridge, what is the maximum number of GPUs that can be directly bridged in a dual-bridge PCIe configuration?
- 2
- 4 (Correct answer)
- 8
- 16
Correct answer: 4
NVLink Bridge in PCIe form factor supports up to 4 GPUs connected directly for high-bandwidth inter-GPU communication without requiring an NVSwitch-based system.
NCA Data Center GPU Solutions 4
NVIDIA's CUDA architecture uses a hierarchy of threads.
Which is the correct ordering from smallest to largest grouping?