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NVIDIA-Certified Associate AI Infrastructure and Operations Sample Questions (Q44-Q49):
NEW QUESTION # 44
Which of the following statements is true about GPUs and CPUs?
Answer: D
Explanation:
GPUs and CPUs are architecturally distinct due to their optimization goals. GPUs feature thousands of simpler cores designed for massive parallelism, excelling at executing many lightweight threads concurrently-ideal for tasks like matrix operations in AI. CPUs, conversely, have fewer, more complex cores optimized for sequential processing and handling intricate control flows, making them suited for serial tasks.
This divergence in design means GPUs outperform CPUs in parallel workloads, while CPUs excel in single- threaded performance, contradicting claims of identical architectures or interchangeable use.
(Reference: NVIDIA GPU Architecture Whitepaper, Section on GPU vs. CPU Design)
NEW QUESTION # 45
You are deploying an AI model on a cloud-based infrastructure using NVIDIA GPUs. During the deployment, you notice that the model's inference times vary significantly across different instances, despite using the same instance type. What is the most likely cause of this inconsistency?
Answer: D
Explanation:
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.
NEW QUESTION # 46
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)
Answer: B,C
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 # 47
What factors have led to significant breakthroughs in Deep Learning?
Answer: C
Explanation:
Deep learning breakthroughs stem from three pillars: advances in hardware (e.g., GPUs and TPUs) providing the compute power for large-scale neural networks; the availability of large datasets offering the data volume needed for training; and improvements in training algorithms (e.g., optimizers like Adam, novel architectures like Transformers) enhancing model efficiency and accuracy. While internet speed, sensors, or smartphones play roles in broader tech, they're less directly tied to deep learning's core advancements.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Deep Learning Advancements)
NEW QUESTION # 48
You are managing an AI cluster with several nodes, each equipped with multiple NVIDIA GPUs. The cluster supports various machine learning tasks with differing resource requirements. Some jobs are GPU-intensive, while others require high memory but minimal GPU usage. Your goal is to efficiently allocate resources to maximize throughput and minimize job wait times. Which orchestration strategy would best optimize resource allocation in this mixed-workload environment?
Answer: C
Explanation:
Using a dynamic scheduler that adjusts resource allocation based on job requirements and current cluster utilization is the best strategy for optimizing resource allocation in a mixed-workload AI cluster with NVIDIA GPUs. Tools like NVIDIA's GPU Operator with Kubernetes enable dynamic scheduling, matching GPU- intensive jobs to available compute resources and memory-heavy jobs to nodes with sufficient capacity, maximizing throughput and minimizing wait times. Option A (manual assignment) is inefficient and error- prone in a dynamic environment. Option C (even allocation) ignores job-specific needs, leading to underutilization or contention. Option D (fixed priority) lacks adaptability to resource demands. NVIDIA's orchestration documentation emphasizes dynamic scheduling for heterogeneous workloads.
NEW QUESTION # 49
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