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Questions # 11:

What steps should an administrator take if they encounter errors related to RDMA (Remote Direct Memory Access) when using Magnum IO?

Options:

A.

Increase the number of network interfaces on each node to handle more traffic concurrently without using RDMA.

B.

Disable RDMA entirely and rely on TCP/IP for all network communications between nodes.

C.

Check that RDMA is properly enabled and configured on both storage and compute nodes for efficient data transfers.

D.

Reboot all compute nodes after every job completion to reset RDMA settings automatically.

Expert Solution
Questions # 12:

In a high availability (HA) cluster, you need to ensure that split-brain scenarios are avoided.

What is a common technique used to prevent split-brain in an HA cluster?

Options:

A.

Configuring manual failover procedures for each node.

B.

Using multiple load balancers to distribute traffic evenly across nodes.

C.

Implementing a heartbeat network between cluster nodes to monitor their health.

D.

Replicating data across all nodes in real time.

Expert Solution
Questions # 13:

A Slurm user needs to display real-time information about the running processes and resource usage of a Slurm job.

Which command should be used?

Options:

A.

smap -j

B.

scontrol show job

C.

sstat -j

D.

sinfo -j

Expert Solution
Questions # 14:

An administrator needs to submit a script named “my_script.sh” to Slurm and specify a custom output file named “output.txt” for storing the job's standard output and error.

Which ‘sbatch’ option should be used?

Options:

A.

=-o output.txt

B.

=-e output.txt

C.

=-output-output output.txt

Expert Solution
Questions # 15:

Your organization is running multiple AI models on a single A100 GPU using MIG in a multi-tenant environment. One of the tenants reports a performance issue, but you notice that other tenants are unaffected.

What feature of MIG ensures that one tenant's workload does not impact others?

Options:

A.

Hardware-level isolation of memory, cache, and compute resources for each instance.

B.

Dynamic resource allocation based on workload demand.

C.

Shared memory access across all instances.

D.

Automatic scaling of instances based on workload size.

Expert Solution
Questions # 16:

You are setting up a Kubernetes cluster on NVIDIA DGX systems using BCM, and you need to initialize the control-plane nodes.

What is the most important step to take before initializing these nodes?

Options:

A.

Set up a load balancer before initializing any control-plane node.

B.

Disable swap on all control-plane nodes before initializing them.

C.

Ensure that Docker is installed and running on all control-plane nodes.

D.

Configure each control-plane node with its own external IP address.

Expert Solution
Questions # 17:

A system administrator is troubleshooting a Docker container that is repeatedly failing to start. They want to gather more detailed information about the issue by generating debugging logs.

Why would generating debugging logs be an important step in resolving this issue?

Options:

A.

Debugging logs disable other logging mechanisms, reducing noise in the output.

B.

Debugging logs provide detailed insights into the Docker daemon's internal operations.

C.

Debugging logs prevent the container from being removed after it stops, allowing for easier inspection.

D.

Debugging logs fix issues related to container performance and resource allocation.

Expert Solution
Questions # 18:

You are deploying AI applications at the edge and want to ensure they continue running even if one of the servers at an edge location fails.

How can you configure NVIDIA Fleet Command to achieve this?

Options:

A.

Use Secure NFS support for data redundancy.

B.

Set up over-the-air updates to automatically restart failed applications.

C.

Enable high availability for edge clusters.

D.

Configure Fleet Command's multi-instance GPU (MIG) to handle failover.

Expert Solution
Questions # 19:

You are managing a Kubernetes cluster running AI training jobs using TensorFlow. The jobs require access to multiple GPUs across different nodes, but inter-node communication seems slow, impacting performance.

What is a potential networking configuration you would implement to optimize inter-node communication for distributed training?

Options:

A.

Increase the number of replicas for each job to reduce the load on individual nodes.

B.

Use standard Ethernet networking with jumbo frames enabled to reduce packet overhead during communication.

C.

Configure a dedicated storage network to handle data transfer between nodes during training.

D.

Use InfiniBand networking between nodes to reduce latency and increase throughput for distributed training jobs.

Expert Solution
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