Question 1

You are tasked with optimizing the performance of a deep learning model used for image recognition. The model needs to process a large dataset as quickly as possible while maintaining high accuracy. You have access to both GPU and CPU resources. Which two statements best describe why GPUs are more suitable than CPUs for this task? (Select two)

Options :

A : CPUs are better suited for handling the large dataset due to their superior memory bandwidth.

B :

GPUs have a higher number of cores compared to CPUs, allowing for parallel processing of many operations simultaneously.

C :

GPUs are optimized for matrix operations, which are common in deep learning algorithms.

D : GPUs have a lower latency than CPUs, making them faster for individual calculations.

E :

CPUs consume less power than GPUs, making them more suitable for prolonged computations.

Answer: B,C

Question 2

In an AI data center, you are working with a professional administrator to optimize the deployment of AI workloads across multiple servers. Which of the following actions would best contribute to improving the efficiency and performance of the data center?

Options :

A :

Distribute AI workloads across multiple servers with GPUs, while using DPUs to manage network and storage tasks

B : Consolidate all AI workloads onto a single high-performance server to maximize GPU utilization

C :

Allocate all networking tasks to the CPUs, allowing the GPUs and DPUs to focus solely on AI model computation

D :

[Note: Original question only provided three options; assuming a typo and treating A as the intended correct answer]

Answer: A

Question 3

You have deployed an AI training job on a GPU cluster, but the training time has not decreased as expected after adding more GPUs. Upon further investigation, you observe that the GPU utilization is low, and the CPU utilization is very high. What is the most likely cause of this issue?

Options :

A : The AI model is not compatible with multi-GPU training.

B : The GPUs are not properly connected in the cluster.

C : Incorrect software version installed on the GPUs.

D :

The data preprocessing is being bottlenecked by the CPU.

Answer: D

Question 4

In an effort to improve energy efficiency in your AI infrastructure using NVIDIA GPUs, you're considering several strategies. Which of the following would most effectively balance energy efficiency with maintaining performance?

Options :

A : Enabling deep sleep mode on all GPUs during processing times

B : Disabling all energy-saving features to ensure maximum performance

C : Running all GPUs at the lowest possible clock speeds

D : Employing NVIDIA GPU Boost technology to dynamically adjust clock speeds

Answer: D

Question 5

You are part of a team investigating the performance variability of an AI model across different hardware configurations. The model is deployed on various servers with differing GPU types, memory sizes, and CPU clock speeds. Your task is to identify which hardware factors most significantly impact the model's inference time. Which analysis approach would be most effective in identifying the hardware factors that significantly impact the model’s inference time?

Options :

A : Conduct a t-test comparing inference times between two different GPU types.

B : Perform a multiple regression analysis with inference time as the dependent variable.

C : Apply clustering to group hardware configurations by inference time.

D : Create a bar chart comparing average inference times across hardware configurations.

Answer: B