Free NVIDIA-Certified Associate AI Infrastructure and Operations Practice Questions

A.  

8

B.  

16

C.  

4

A.  

It allocates resources based on which job requests came first.

B.  

It ensures that all jobs in the cluster are executed simultaneously.

C.  

It increases the number of resources available in the cluster.

D.  

It allocates resources efficiently and optimizes job execution.

A.  

It automatically updates the Kubernetes version across all nodes.

B.  

It simplifies the management and deployment of NVIDIA GPU software components.

C.  

It increases the processing power of CPUs within the Kubernetes cluster.

D.  

It provides automatic scaling of NVIDIA GPU resources based on application demand.

A.  

7

B.  

8

C.  

4

A.  

Processing speed, storage capacity, and network connectivity.

B.  

Power, cooling, and physical space.

C.  

Bandwidth, security, and redundancy.

A.  

Moore’s Law

B.  

Rule-based machine learning

C.  

High-powered GPUs

D.  

Large amounts of data

A.  

It is bare-metal based but it supports containers.

B.  

It has advanced scheduling capabilities to assign jobs to available resources.

C.  

It has no inferencing capabilities.

D.  

It does load balancing to distribute traffic across containers.

A.  

BERT Large model

B.  

State space model

C.  

Transformer model

D.  

Attention model

A.  

Slurm is used for data storage and retrieval in an AI cluster.

B.  

Slurm is responsible for AI model training and inference in an AI cluster.

C.  

Slurm is used for interconnecting nodes in an AI cluster.

D.  

Slurm helps with managing job scheduling and resource allocation in the cluster.

A.  

The maximum amount of time a GPU will be used for a workload.

B.  

The GPU memory in use compared to available GPU memory.

C.  

The percentage of time the GPU is actively processing data.

D.  

The number of GPU cores available to the workload.