Question 1

Which networking feature is MOST important for supporting distributed training of large AI models across multiple data centers?

Options :

A : High throughput with low latency WAN links between data centers

B : Implementation of Quality of Service (QoS) policies to prioritize AI training traffic

C : Segregated network segments to prevent data leakage between AI tasks

D : Deployment of wireless networking to enable flexible node placement

Answer: A

Question 2

You are responsible for optimizing the energy efficiency of an AI data center that handles both training and inference workloads. Recently, you have noticed that energy costs are rising, particularly during peak hours, but performance requirements are not being met. Which approach would best optimize energy usage while maintaining performance levels?

Options :

A : Transition all workloads to CPUs during peak hours to reduce GPU power consumption.

B : Use liquid cooling to lower the temperature of GPUs and reduce their energy consumption.

C : Implement a workload scheduling system that shifts non-urgent training jobs to off-peak hours.

D : Lower the power limit on all GPUs to reduce their maximum energy consumption during all operations.

Answer: C

Question 3

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 4

Which two components are included in GPU Operator? (Choose two.)

Options :

A : Drivers

B : PyTorch

C : DCGM

D : TensorFlow

Answer: A,C

Question 5

You have completed a data mining project and have discovered several key insights from a large and complex dataset. You now need to present these insights to stakeholders in a way that clearly communicates the findings and supports data-driven decision-making. Which of the following approaches would be most effective for visualizing insights from large datasets to support decision-making in AI projects? (Select two)

Options :

A : Visualize all data in a single pie chart.

B : Use a heatmap to represent correlations between variables.

C : Generate a detailed text report with all the raw data.

D : Create interactive dashboards using tools like Tableau or Power BI.

E : Present a simple line chart showing one aspect of the data.

Answer: B,D

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