Question 1

Which of the following statements is true about GPUs and CPUs?

Options :

A : GPUs are optimized for parallel tasks, while CPUs are optimized for serial tasks.

B : GPUs have very low bandwidth main memory while CPUs have very high bandwidth main memory.

C : GPUs and CPUs have the same number of cores, but GPUs have higher clock speeds.

D : GPUs and CPUs have identical architectures and can be used interchangeably.

Answer: A

Question 2

You are assisting a senior data scientist in analyzing a large dataset of customer transactions to identify potential fraud. The dataset contains several hundred features, but the senior team member advises you to focus on feature selection before applying any machine learning models. Which approach should you take under their supervision to ensure that only the most relevant features are used?

Options :

A : Use correlation analysis to identify and remove features that are highly correlated with each other.

B : Select features randomly to reduce the number of features while maintaining diversity.

C : Ignore the feature selection step and use all features in the initial model.

D : Use Principal Component Analysis (PCA) to reduce the dataset to a single feature.

Answer: A

Question 3

Which are three key features of InfiniBand networking technology?

Options :

A : High reliability, high latency, and CPU offloads.

B : High latency, high reliability, and high bandwidth.

C : GPU offloads, low latency, high reliability.

D : Low latency, high bandwidth, and CPU offloads.

Answer: D

Question 4

Your AI data center is running multiple high-performance GPU workloads, and you notice that certain servers are being underutilized while others are consistently at full capacity, leading to inefficiencies. Which of the following strategies would be most effective in balancing the workload across your AI data center?

Options :

A : Use Horizontal Scaling to Add More Servers

B : Increase Cooling Capacity in the Data Center

C : Implement NVIDIA GPU Operator with Kubernetes for Automatic Resource Scheduling

D : Manually Reassign Workloads Based on Current Utilization

Answer: C

Question 5

You are managing a high-performance AI infrastructure that leverages NVIDIA GPUs for deep learning training workloads. Your team is experiencing significant bottlenecks in data loading, causing GPUs to be underutilized during training sessions. You suspect that the storage solution might be contributing to the issue. Which of the following changes would most likely optimize GPU utilization and reduce data loading bottlenecks?

Options :

A : Reduce the batch size in your training jobs.

B : Increase the number of GPUs in the system.

C : Upgrade to a faster storage solution like NVMe SSDs.

D : Enable data augmentation to increase dataset variety.

Answer: C

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