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最新的 NVIDIA-Certified Associate NCA-AIIO 免費考試真題:

1. A data center is designed to support large-scale AI training and inference workloads using a combination of GPUs, DPUs, and CPUs. During peak workloads, the system begins to experience bottlenecks. Which of the following scenarios most effectively uses GPUs and DPUs to resolve the issue?

A) Redistribute computational tasks from GPUs to DPUs to balance the workload evenly between both
B) Transfer memory management from GPUs to DPUs to reduce the load on GPUs during peak times
C) Offload network, storage, and security management from the CPU to the DPU, freeing up the CPU and GPU to focus on AI computation
D) Use DPUs to take over the processing of certain AI models, allowing GPUs to focus solely on high- priority tasks

2. You are working with a large healthcare dataset containing millions of patient records. Your goal is to identify patterns and extract actionable insights that could improve patient outcomes. The dataset is highly dimensional, with numerous variables, and requires significant processing power to analyze effectively.
Which two techniques are most suitable for extracting meaningful insights from this large, complex dataset?
(Select two)

A) SMOTE (Synthetic Minority Over-sampling Technique)
B) K-means Clustering
C) Data Augmentation
D) Batch Normalization
E) Dimensionality Reduction (e.g., PCA)

3. You are managing an AI data center platform that runs a mix of compute-intensive training jobs and low- latency inference tasks. Recently, the system has been experiencing unexpected slowdowns during inference tasks, even though there are sufficient GPU resources available. What is the most likely cause of this issue, and how can it be resolved?

A) The inference tasks are not optimized for the GPU architecture, leading to inefficient use of resources.
B) The inference jobs are running at the same priority level as the training jobs, causing contention for resources.
C) The training jobs are consuming too much network bandwidth, leaving insufficient bandwidth for inference data transfer.
D) The GPUs are overheating, leading to thermal throttling during inference.

4. You are managing an AI training workload that requires high availability and minimal latency. The data is stored across multiple geographically dispersed data centers, and the compute resources are provided by a mix of on-premises GPUs and cloud-based instances. The model training has been experiencing inconsistent performance, with significant fluctuations in processing time and unexpected downtime. Which of the following strategies is most effective in improving the consistency and reliability of the AI training process?

A) Migrating all data to a centralized data center with high-speed networking
B) Implementing a hybrid load balancer to dynamically distribute workloads across cloud and on-premises resources
C) Switching to a single-cloud provider to consolidate all compute resources
D) Upgrading to the latest version of GPU drivers on all machines

5. You are managing an AI cluster where multiple jobs with varying resource demands are scheduled. Some jobs require exclusive GPU access, while others can share GPUs. Which of the following job scheduling strategies would best optimize GPU resource utilization across the cluster?

A) Enable GPU sharing and use NVIDIA GPU Operator with Kubernetes
B) Schedule all jobs with dedicated GPU resources
C) Use FIFO (First In, First Out) Scheduling
D) Increase the default pod resource requests in Kubernetes

問題與答案：