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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q35-Q40):

NEW QUESTION # 35
Which type of GPU core was specifically designed to realistically simulate the lighting of a scene?

A. Tensor Cores
B. CUDA Cores
C. Ray Tracing Cores

Answer: C

Explanation:
Ray Tracing Cores, introduced in NVIDIA's RTX architecture, are specialized hardware units built to accelerate ray-tracing computations-simulating light interactions (e.g., reflections, shadows) for photorealistic rendering in real time. CUDA Cores handle general-purpose parallel tasks, and Tensor Cores optimize matrix operations for AI, but only Ray Tracing Cores target lighting simulation.
(Reference: NVIDIA GPU Architecture Whitepaper, Section on Ray Tracing Cores)

NEW QUESTION # 36
You are managing an AI data center where energy consumption has become a critical concern due to rising costs and sustainability goals. The data center supports various AI workloads, including model training, inference, and data preprocessing. Which strategy would most effectively reduce energy consumption without significantly impacting performance?

A. Implement dynamic voltage and frequency scaling (DVFS) to adjust GPU power usage based on workload demands.
B. Reduce the clock speed of all GPUs to lower power consumption.
C. Schedule all AI workloads during nighttime to take advantage of lower electricity rates.
D. Consolidate all AI workloads onto a single GPU to reduce overall power usage.

Answer: A

Explanation:
Dynamic Voltage and Frequency Scaling (DVFS) allows GPUs to adjust their power usage dynamically based on workload intensity, reducing energy consumption during low-demand periods while maintaining performance when needed. NVIDIA GPUs, such as those in DGX systems, support DVFS through tools like NVIDIA Management Library (NVML) and nvidia-smi, enabling fine-tuned power management. This approach balances efficiency and performance, critical for diverse AI workloads like training (high compute) and inference (variable demand), aligning with NVIDIA's energy-efficient computing initiatives.
Consolidating workloads onto a single GPU (Option A) risks overloading it, degrading performance and negating energy savings due to inefficiency. Scheduling workloads at night (Option C) addresses cost but not total consumption or sustainability, and it may delay time-sensitive tasks. Reducing clock speed universally (Option D) lowers power use but sacrifices performance across all workloads, which is impractical for an AI data center. DVFS is the most effective NVIDIA-supported strategy here.

NEW QUESTION # 37
A customer is evaluating an AI cluster for training and is questioning why they should use a large number of nodes. Why would multi-node training be advantageous?

A. The model is too large to fit into GPU memory.
B. The model is being used by a large number of users.
C. The model is being used for large-scale inference workloads.

Answer: A

Explanation:
Multi-node training is advantageous when a model's size-its parameters, activations, and gradients- exceeds the memory capacity of a single GPU. By sharding the model across multiple nodes (using techniques like data parallelism or model parallelism), training becomes feasible and efficient. User count and inference scale are unrelated to training architecture needs, which focus on compute and memory distribution.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Multi-Node Training Benefits)

NEW QUESTION # 38
A large enterprise is deploying a high-performance AI infrastructure to accelerate its machine learning workflows. They are using multiple NVIDIA GPUs in a distributed environment. To optimize the workload distribution and maximize GPU utilization, which of the following tools or frameworks should be integrated into their system? (Select two)

A. TensorFlow Serving
B. NVIDIA NGC (NVIDIA GPU Cloud)
C. NVIDIA CUDA
D. Keras
E. NVIDIA NCCL (NVIDIA Collective Communications Library)

Answer: C,E

Explanation:
In a distributed environment with multiple NVIDIA GPUs, optimizing workload distribution and GPU utilization requires tools that enable efficient computation and communication:
* NVIDIA CUDA(A) is a foundational parallel computing platform that allows developers to harness GPU power for general-purpose computing, including machine learning. It's essential for programming GPUs and optimizing workloads in a distributed setup.
* NVIDIA NCCL(D) (NVIDIA Collective Communications Library) is designed for multi-GPU and multi-node communication, providing optimized primitives (e.g., all-reduce, broadcast) for collective operations in deep learning. It ensures efficient data exchange between GPUs, maximizing utilization in distributed training.
* NVIDIA NGC(B) is a hub for GPU-optimized containers and models, useful for deployment but not directly responsible for workload distribution or GPU utilization optimization.
* TensorFlow Serving(C) is a framework for deploying machine learning models for inference, not for optimizing distributed training or GPU utilization during model development.
* Keras(E) is a high-level API for building neural networks, but it lacks the low-level control needed for distributed workload optimization-it relies on backends like TensorFlow or CUDA.
Thus, CUDA (A) and NCCL (D) are the best choices for this scenario.

NEW QUESTION # 39
You are managing an AI infrastructure using NVIDIA GPUs to train large language models for a social media company. During training, you observe that the GPU utilization is significantly lower than expected, leading to longer training times. Which of the following actions is most likely to improve GPU utilization and reduce training time?

A. Increase the batch size during training
B. Reduce the learning rate
C. Decrease the model complexity
D. Use mixed precision training

Answer: D

Explanation:
Using mixed precision training (A) is most likely to improve GPU utilization and reduce training time. Mixed precision combines FP16 and FP32 computations, leveraging NVIDIA Tensor Cores (e.g., in A100 GPUs) to perform more operations per cycle. This increases throughput, reduces memory usage, and keeps GPUs busier, addressing low utilization. It's widely supported in frameworks like PyTorch and TensorFlow via NVIDIA's Apex or automatic mixed precision (AMP).
* Decreasing model complexity(B) might speed up training but sacrifices accuracy, not addressing utilization directly.
* Increasing batch size(C) can improve utilization but risks memory overflows if too large, and doesn't optimize compute efficiency like mixed precision.
* Reducing learning rate(D) affects convergence, not GPU utilization.
NVIDIA promotes mixed precision for large language models (A).

NEW QUESTION # 40
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