Which of the following accurately describes the NVIDIA DGX BasePOD reference architecture
It is a standalone AI software solution without any hardware integration.
It consists of NVIDIA DGX systems, networking systems, and storage solutions, excluding software.
It allows the flexibility of an AI infrastructure based on NVIDIA DGX systems combined with 3rd party networking.
It provides infrastructure, networking, and software components to accelerate deployment and execution of AI workloads.2.
Question 2
What is the main purpose of NVIDIA's NVLink interconnect in a multi-GPU setup?

To allocate power resources evenly among GPUs to prevent overheating.
To communicate between the GPU and a CPU, surpassing PCIe speeds.
To enable high-speed data transfer and communication between GPUs, significantly faster than PCIe speeds.
To optimize GPU memory management for efficient data storage and retrieval.3.
Question 3
How does InfiniBand enhance the performance of AI workloads?

By offering low latency, high throughput, and intelligent offloading features for optimized data processing
By offering high latency, low throughput, and intelligent offloading features for optimized data processing.
By providing CPU-to-GPU communication at incredibly high speeds.
By prioritizing data transmission based on computational requirements.4.
Question 4
In the past six months, your cloud-based AI project has grown substantially, with increased scale and expenses. What started as experimental with sporadic GPU usage has now evolved into a more complex phase requiring frequent iterations of complex models. Concurrently, escalating GPU and storage costs, coupled with rising latency, are impacting the overall models performance.

What strategy would you recommend to manage costs and optimize performance in the cloud for your AI project?

Adopt a hybrid approach where you develop and iterate on-premises, then deploy at scale in the cloud to balance cost and performance needs.
Consider moving the entire AI project to an on-premises environment to gain better control over costs and performance.
Consider moving to a cloud provider with more competitive pricing and better performance to reduce costs and improve project efficiency.
Open a support ticket with the cloud provider to investigate and address latency and cost issues affecting the project.5.
Question 5
Which of the following exemplifies the application of AI in the healthcare sector?
Enhancing communication between medical professionals and patients.
Enabling real-time image analysis for cancer cell identification.
Managing patients' medical appointments.
Automating administrative tasks in hospital management systems.6.
Question 6
Why are workload management and monitoring considered critical tasks in an AI cluster?

They ensure efficient resource allocation, job scheduling, and checking resource usage.
They optimize energy consumption and reduce operational costs in the AI cluster.
They involve ensuring proper hardware installation and maintenance within the cluster.
They facilitate communication between different components of the AI cluster.7.
Question 7
What are the benefits of using GPUs to run AI workloads? 

GPUs provide the parallel processing power required to train AI models.
GPUs offer enhanced compatibility with legacy hardware systems, simplifying integration processes.
GPUs are optimized for serial tasks required by AI models.
GPUs have large main memory, so trying to access data results in fewer cache misses. 8.
Question 8
When deploying GPUs in a server system, what factors should you consider for efficient configuration?
Optimize power supply to maximize energy consumption.
Balance GPU configurations based on workload requirements.
Choose the largest possible GPU models.
Use only air cooling for GPUs.9.
Question 9
Which is a benefit that organizations can achieve when using NGC containers? 
NGC containers help with faster and more portable development and deployment of AI applications on GPUs.
NGC containers offer accelerated applications and tools to run health care workloads.
NGC containers provide a job scheduler to manage the allocation of resources and launch jobs on a cluster.
NGC containers are a cloud-native, multi-GPU enabled, open platform for virtual collaboration and real-time photorealistic simulation.10.
Question 10
What are the key considerations for storage infrastructure for AI workloads?
Throughput is the most important consideration.
Brand familiarity to the storage solution vendor.
Speed, caching, and distributed storage.
The capacity of the storage devices.11.
Question 11
Which of the following statements best addresses the considerations of running AI workloads on Ethernet networks?
Ethernet networks offer sufficient bandwidth for AI workloads, ensuring smooth data transmission.
AI workloads are incompatible with Ethernet networks due to their limited data processing capabilities.
Ethernet networks, which are built for general clouds or traditional data centers, are too slow for AI workloads.
Ethernet networks, which are built for general clouds or traditional data centers, provide reliable support for large workloads such as generative AI.12.
Question 12
Which storage solution offers the best overall performance for AI workloads?
A storage solution that offers fast read IO along with data caching.
A storage solution that offers the best read and re-read performance.
A storage solution that manages data as objects.
A storage solution that offers the highest bandwidth.13.
Question 13
How are Large Language Models (LLMs) utilized in generative artificial intelligence (GenAI)?

LLMs are exclusively used for image recognition tasks.
LLMs are used to generate human-like text based on input prompts.
LLMs are employed for highly accurate and complex math calculations.
LLMs are used to generate random sequences of words without any specific context.14.
Question 14
Which of the following statements best reflects the thermal characteristics of GPUs?

GPUs are known for their high heat output relative to other computing components.
GPUs efficiently convert power to heat, leading to reduced heat output.
GPUs generate minimal heat due to their efficient power utilization.
GPUs consume power primarily for computational tasks, minimizing heat generation.15.
Question 15
Which feature best characterizes NVIDIA NVSwitch technology?

It provides GPU-to-GPU communication at incredibly high speeds, especially during "all to all" operations. 
It provides advanced security features for GPU-based applications, to protect sensitive data during communication.
It enhances the processing power of individual GPUs by optimizing memory management.
It enables virtualization of GPU resources, allowing for seamless allocation among multiple virtual machines.16.
Question 16
What application in the 1980's exemplified one of the first uses of AI?
Image classification.
Spam filters.
Object recognition. 
Virtual personal assistants.17.
Question 17
What factor contributed to GPUs becoming essential for the breakthroughs in Deep Learning (DL)?

GPUs facilitate the parallel processing required for training deep neural networks more efficiently.
GPUs are mainly used for data storage of large data sets for deep learning.
GPUs are primarily used for rendering graphics in deep learning, enhancing the visualizations of the models.
GPUs offload the processing of training models with large amounts of data to CPUs to achieve a high degree of parallelism.18.
Question 18
What are some benefits of utilizing reference architectures in designing systems for dense computing environments?

Reference architectures provide a foundational design that can be customized to meet organizational requirements.
Reference architectures outline recommended designs based on average system performance.
Reference architectures decrease cost but also decrease reliability.
Reference architectures are primarily used to increase design complexity and customization.19.
Question 19
What is the difference between generative artificial intelligence (GenAI) and traditional AI?

Generative AI focuses on detecting patterns and making decisions, while traditional AI produces new content based on training data.
Generative AI creates new content based on learned patterns, while traditional AI focuses on detecting patterns and making decisions.
GenAI can perform text classification, but traditional AI can’t.
For many use cases GenAI is less compute intensive compared to traditional AI.20.
Question 20
NVIDIA virtual GPU (vGPU) software offers the following benefits.  Select two. 

Enables multiple virtual machines to have direct access to a single physical GPU.
Provides a set of pre-trained models customized for the cloud.
Provides a set of libraries specialized in model training.
Supports bare-metal performance for compute workloads that are running virtualized.21.
Question 21
Which of the following best defines the role of a data processing unit (DPU) in a modern computing environment?
Accelerating machine learning algorithms for enhanced performance.
Accelerating network traffic and offloading tasks from the server CPU.
Managing GPU cooling and temperature regulation.
Managing graphical processing tasks for improved display performance.22.
Question 22
Which of the following options accurately describes cooling methods for GPU chips? Select two.
Cooling via cold airflow is the accepted standard for high density cooling.
Water-cooled heat exchangers are known for efficient heat rejection and higher cost.
Water-cooled heat exchangers are inexpensive but become inefficient beyond 50 kW per rack.
Cold air is cost-effective but becomes inefficient beyond 30 kW per rack.23.
Question 23
What is the primary purpose of Kubernetes?
Managing the AI development workflow.
Managing and automating containerized applications.
Assigning AI workloads to available compute resources.
Operationalizing ML training and inference.24.
Question 24
Which of the tools below would you use for workload management, scheduling and prioritizing jobs, in an AI Data Center?
Slurm
Ansible
Kubernetes
NVIDIA DCGM25.
Question 25
Which of the following statements accurately describes the NVIDIA Grace Hopper Superchip architecture?

It combines the Grace CPU with a BlueField DPU for optimal performance in machine learning tasks.
It combines the Grace CPU with the H100 GPU through the high-bandwidth NVLink interconnect.
It prioritizes GPU performance over CPU capabilities for graphics-intensive applications.
It focuses on optimizing power efficiency for gaming and multimedia entertainment applications.