Practice Free NCA-AIIO Exam Online Questions
You are managing an AI data center where energy consumption has become a critical concern due to rising costs and sustainability goals. The data center supports various AI workloads, including model training, inference, and data preprocessing.
Which strategy would most effectively reduce energy consumption without significantly impacting performance?
- A . Schedule all AI workloads during nighttime to take advantage of lower electricity rates.
- B . Reduce the clock speed of all GPUs to lower power consumption.
- C . Consolidate all AI workloads onto a single GPU to reduce overall power usage.
- D . Implement dynamic voltage and frequency scaling (DVFS) to adjust GPU power usage based on
real-time workload demands.
When virtualizing a GPU-accelerated infrastructure to support AI operations, what is a key factor to ensure efficient and scalable performance across virtual machines (VMs)?
- A . Increase the CPU allocation to each VM.
- B . Ensure that GPU memory is not overcommitted among VMs.
- C . Enable nested virtualization on the VMs.
- D . Allocate more network bandwidth to the host machine.
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?
- A . The power supply to the GPU nodes is insufficient.
- B . The data being processed includes large datasets that are stored in GPU memory but not efficiently utilized in computation.
- C . The workloads are being run with models that are too small for the available GPUs.
- D . The GPU drivers are outdated and need updating.
You are managing an AI infrastructure that requires optimized resource allocation for a deep learning model training process. The model is expected to process a large dataset with a combination of CPU, GPU, and storage resources.
Which two strategies would most effectively enhance performance and resource utilization? (Select two)
- A . Use network-attached storage (NAS) for dataset access
- B . Disable GPU memory overclocking to prevent instability
- C . Utilize multi-GPU parallel processing for training
- D . Store the dataset on a local SSD drive with high IOPS
- E . Allocate all available CPU resources for training tasks
Your organization runs multiple AI workloads on a shared NVIDIA GPU cluster. Some workloads are more critical than others. Recently, you’ve noticed that less critical workloads are consuming more GPU resources, affecting the performance of critical workloads.
What is the best approach to ensure that critical workloads have priority access to GPU resources?
- A . Implement Model Optimization Techniques
- B . Upgrade the GPUs in the Cluster to More Powerful Models
- C . Use CPU-based Inference for Less Critical Workloads
- D . Implement GPU Quotas with Kubernetes Resource Management
In a scenario where you need to deploy an AI workload on a virtualized infrastructure with GPU acceleration, which of the following are key considerations to ensure optimal performance? (Select two)
- A . Shared GPU Resources
- B . GPU Passthrough
- C . Storage Type (HDD vs SSD)
- D . Oversubscription of CPU
- E . Memory Overcommitment
In an AI environment, the NVIDIA software stack plays a crucial role in ensuring seamless operations across different stages of the AI workflow.
Which components of the NVIDIA software stack would you use to accelerate AI model training and deployment? (Select two)
- A . NVIDIA TensorRT
- B . NVIDIA cuDNN (CUDA Deep Neural Network library)
- C . NVIDIA Nsight
- D . NVIDIA DGX-1
- E . NVIDIA DeepStream SDK
Which statement correctly differentiates between AI, machine learning, and deep learning?
- A . Machine learning is a type of AI that only uses linear models, while deep learning involves non-linear models
- B . Machine learning is the same as AI, and deep learning is simply a method within AI that doesn’t involve machine learning
- C . AI is a broad field encompassing various technologies, including machine learning, which focuses on learning from data, while deep learning is a specialized type of machine learning that uses neural networks
- D . Deep learning is a broader concept than machine learning, which is a specialized form of AI
In an AI-focused data center, ensuring high data throughput is critical for feeding large datasets to training models efficiently.
Which strategy would best optimize data throughput in this environment?
- A . Implement NVMe SSDs for faster data access and higher throughput
- B . Implement a distributed file system without considering the underlying hardware
- C . Use a RAID 5 configuration to increase redundancy and throughput
- D . Use traditional HDD storage systems due to their high storage capacity
Which component of the AI software ecosystem is responsible for managing the distribution of deep learning model training across multiple GPUs?
- A . TensorFlow
- B . CUDA
- C . NCCL
- D . cuDNN