Practice Free NCA-AIIO Exam Online Questions
Which is the best PUE value for a data center?
- A . PUE of 1.2
- B . PUE of 3.5
- C . PUE of 5.0
- D . PUE of 2.0
A
Explanation:
Power Usage Effectiveness (PUE) measures data center efficiency, with an ideal value of 1.0 (all power used by IT equipment). A PUE of 1.2, indicating only 20% overhead, is highly efficient and closer to the ideal than 2.0 (100% overhead), 3.5, or 5.0, making it the best among the options for energy-conscious AI deployments.
(Reference: NVIDIA AI Infrastructure and Operations Study Guide, Section on Data Center Efficiency)
You are managing a data center running numerous AI workloads on NVIDIA GPUs. Recently, some of the GPUs have been showing signs of underperformance, leading to slower job completion times. You suspect that resource utilization is not optimal. You need to implement monitoring strategies to ensure GPUs are effectively utilized and to diagnose any underperformance.
Which of the following metrics is most critical to monitor for identifying underutilized GPUs in your data center?
- A . Network Bandwidth Utilization
- B . System Uptime
- C . GPU Memory Usage
- D . GPU Core Utilization
Your AI cluster handles a mix of training and inference workloads, each with different GPU resource requirements and runtime priorities.
What scheduling strategy would best optimize the allocation of GPU
resources in this mixed-workload environment?
- A . Increase the GPU Memory Allocation for All Jobs
- B . Use Kubernetes Node Affinity with Taints and Tolerations
- C . Manually Assign GPUs to Jobs Based on Priority
- D . Implement FIFO Scheduling Across All Jobs
Which of the following statements is true about GPUs and CPUs?
- 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.
A
Explanation:
GPUs and CPUs are architecturally distinct due to their optimization goals. GPUs feature thousands of simpler cores designed for massive parallelism, excelling at executing many lightweight threads concurrently―ideal for tasks like matrix operations in AI. CPUs, conversely, have fewer, more complex cores optimized for sequential processing and handling intricate control flows, making them suited for serial tasks. This divergence in design means GPUs outperform CPUs in parallel workloads, while CPUs excel in single-threaded performance, contradicting claims of identical architectures or interchangeable use.
(Reference: NVIDIA GPU Architecture Whitepaper, Section on GPU vs. CPU Design)
Which of the following features of GPUs is most crucial for accelerating AI workloads, specifically in the context of deep learning?
- A . Large amount of onboard cache memory.
- B . Lower power consumption compared to CPUs.
- C . High clock speed.
- D . Ability to execute parallel operations across thousands of cores.