Question 1

During routine monitoring of your AI data center, you notice that several GPU nodes are consistently reporting high memory usage but low compute usage. What is the most likely cause of this situation?

Options :

A : The power supply to the GPU nodes is insufficient

B : The GPU drivers are outdated and need updating

C : The workloads are being run with models that are too small for the available GPUs

D :

The data being processed includes large datasets that are stored in GPU memory but not efficiently utilized by the compute cores

Answer: D

Question 2

You are designing a data center platform for a large-scale AI deployment that must handle unpredictable spikes in demand for both training and inference workloads. The goal is to ensure that the platform can scale efficiently without significant downtime or performance degradation. Which strategy would best achieve this goal?

Options :

A : Deploy a fixed number of high-performance GPU servers with auto-scaling based on CPU usage.

B :

Implement a round-robin scheduling policy across all servers to distribute workloads evenly.

C : Migrate all workloads to a single, large cloud instance with multiple GPUs to handle peak loads.

D :

Use a hybrid cloud model with on-premises GPUs for steady workloads and cloud GPUs for scaling during demand spikes.

Answer: D

Question 3

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 4

You are working on a regression task to predict car prices. Model Gamma has a Mean Absolute Error (MAE) of $1,200, while Model Delta has a Mean Absolute Error (MAE) of $1,500. Which model should be preferred based on the Mean Absolute Error (MAE), and what does this metric indicate?

Options :

A : Model Gamma is worse because lower MAE can indicate overfitting.

B : Model Gamma is better because it has a lower MAE.

C : Neither model is better because MAE is not suitable for comparing regression models.

D : Model Delta is better because it has a higher MAE, which means it-s more flexible.

Answer: B

Question 5

A company is deploying a large-scale AI training workload that requires distributed computing across multiple GPUs. They need to ensure efficient communication between GPUs on different nodes and optimize the training time. Which of the following NVIDIA technologies should they use to achieve this?

Options :

A : NVIDIA NVLink

B : NVIDIA TensorRT

C :

NVIDIA NCCL (NVIDIA Collective Communication Library)

D : NVIDIA DeepStream SDK

Answer: C

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