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B : The GPU drivers are outdated and need updating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 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 3

Which of the following factors has most significantly contributed to the recent rapid improvements and widespread adoption of AI?

Options :

A : The invention of new AI programming languages.

B : The rise of social media, increasing the need for AI.

C : Increased computational power, especially with the advent of modern GPUs and specialized AI hardware.

D : The global standardization of AI ethics guidelines.

Answer: C

Question 4

You are tasked with managing an AI training environment where multiple deep learning models are being trained simultaneously on a shared GPU cluster. Some models require more GPU resources and longer training times than others. Which orchestration strategy would best ensure that all models are trained efficiently without causing delays for high-priority workloads?

Options :

A : Implement a priority-based scheduling system that allocates more GPUs to high-priority models while throttling lower-priority ones.

B : Assign equal GPU resources to all models regardless of their requirements.

C : Use a first-come, first-served (FCFS) scheduling policy for all models.

D : Randomly assign GPU resources to each model training job.

Answer: A

Question 5

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

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