Question 1

In a data center designed for AI workloads, what is a key difference in how GPUs and DPUs complement CPU functionality?

Options :

A :

GPUs and DPUs are used interchangeably, depending on the specific AI workload, without any significant difference in function.

B :

GPUs focus on memory management, whereas DPUs focus on accelerating storage throughput for CPUs.

C :

GPUs enhance floating-point computation, while DPUs enhance integer computation, both directly supporting CPU tasks.

D :

GPUs are designed for parallel processing of AI models, while DPUs manage data center networking and security tasks to offload CPUs.

Answer: D

Question 2

While monitoring your AI data center, you observe that one of your GPU clusters is experiencing frequent GPU memory errors. These errors are causing job failures and system instability. What is the most likely cause of these memory errors?

Options :

A : A bug in the deep learning model code.

B : Using outdated versions of GPU drivers.

C : Overheating of the GPUs due to insufficient cooling.

D : Insufficient power supply to the GPU nodes.

Answer: C

Question 3

You are assisting a professional administrator in ensuring data integrity during AI model training in an AI data center. Which of the following strategies would best contribute to maintaining data integrity across distributed GPU nodes?

Options :

A :

Use a single master node with GPUs to manage all data processing and then distribute the results to other nodes

B : Assign data verification tasks to DPUs, allowing GPUs to focus solely on model training

C : Utilize redundant GPU nodes to independently process data and compare results post-training

D :

Implement a distributed file system with replication, ensuring that each GPU node has access to the same consistent dataset

Answer: D

Question 4

Your organization is setting up an AI model deployment pipeline that requires frequent updates. The team needs to ensure minimal downtime during model updates, version control, and monitoring of the models in production. Which software component would be most suitable to handle these requirements?

Options :

A : NVIDIA NGC Catalog

B : NVIDIA TensorRT

C :

NVIDIA Triton Inference Server

D :

NVIDIA DIGITS

Answer: C

Question 5

You are planning to deploy a large-scale AI training job in the cloud using NVIDIA GPUs. Which of the following factors is most crucial to optimize both cost and performance for your deployment?

Options :

A : Selecting instances with the highest available GPU core count

B : Enabling autoscaling to dynamically allocate resources based on workload demand

C : Ensuring data locality by choosing cloud regions closest to your data sources

D : Using reserved instances instead of on-demand instances

Answer: B

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