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NVIDIA AI Operations Sample Questions (Q35-Q40):

NEW QUESTION # 35
You have a cluster dedicated to AI inference, serving models from a persistent volume. You're experiencing high latency and CPU usage on the nodes serving inference requests. You suspect that storage access patterns are contributing to the issue. Your persistent volume is backed by a distributed file system. Describe a strategy, including relevant tools and techniques, to analyze the storage I/O profile of your inference workloads and identify potential optimizations.

A. Utilize the distributed file system's monitoring tools (if available) to analyze I/O patterns at the file system level. This can reveal hotspots or inefficient data access patterns.
B. Use 'iotop' or 'iostat' on the compute nodes to monitor real-time I/O activity and identify processes with high disk I/O. Then check the related containers that are doing more of these reads/writes.
C. Randomly restart the inference pods. If the issue goes away, it means the storage system was temporarily overloaded.
D. Implement storage QOS (Quality of Service) policies to prioritize inference workloads and limit the impact of other I/O-intensive processes.
E. Capture network traffic using 'tcpdump' or Wireshark to analyze the communication patterns between the compute nodes and the storage system. Look for excessive network latency or congestion. Also monitor the network latency using tools like 'ping' or 'iperf.

Answer: A,B,D,E

Explanation:
'iotopTiostat' identifies I/O-heavy processes. 'tcpdump'/Wireshark/ping/iperf helps analyze network communication. File system monitoring tools reveal data access patterns. Implementing storage QOS prioritizes inference workloads. Only restart the inference pods if you have a strong reason, otherwise troubleshooting the storage using one of the other methods is best practice.

NEW QUESTION # 36
You are using BCM to provision a multi-node Kubernetes cluster on NVIDIA DGX servers. One of the nodes consistently fails to join the cluster. You've verified network connectivity and DNS resolution. The 'kubelet' logs show errors related to certificate signing. Which of the following steps is MOST likely to resolve this issue?

A. Re-initialize the Kubernetes control plane using 'kubeadm init and regenerate the join token.
B. Restart the ' kube-proxy' service on the control plane node to refresh the certificate authority.
C. Manually copy the CA certificate from the control plane node to the failing worker node.
D. Disable TLS verification for the kubelet on the failing node (not recommended for production).
E. Approve the pending certificate signing request (CSR) for the failing node using 'kubectl certificate approve

Answer: E

Explanation:
When a node fails to join the cluster due to certificate signing issues, it typically means the kubelet has requested a certificate from the Kubernetes API server, but that request has not been approved. Approving the pending CSR using 'kubectl certificate approve' is the standard way to resolve this issue. A (Regenerating the token is less likely since the token may still be valid), C (Manual copy is not scalable), D (disabling TLS is insecure), and E (kube-proxy is not related to cert signing process).

NEW QUESTION # 37
When deploying a DOCA application using the command, which of the following parameters are mandatory for specifying the DOCA core application's entry point?

A. '-pci-address Defines the PCl address of the BlueField DPLJ where the application will run.
B. '-class The DOCAApplication class in the DOCA executable
C. -num-cores Specifies the number of CPU cores to be allocated to the DOCA application.
D. '-log-level ' : Sets the verbosity level for logging messages from the DOCA application.
E. -entry-point Specifies the name of the function within the DOCA application that should be executed as the entry point.

Answer: B,E

Explanation:
The zentry-point' parameter is required to specify the function that will be the entry point of the DOCA core application. '--class' parameter is mandatory.

NEW QUESTION # 38
You're deploying a deep learning model training job to your Kubernetes cluster managed through BCM. This job requires exclusive access to two GPUs on a specific node with high memory bandwidth. How would you best configure your pod to achieve this?

A. Use a 'nodeSelector' to target the specific node and request 'nvidia.com/gpu: 2 in the pod's resource requests.
B. Use a 'nodeAffinity' with 'requiredDuringSchedulinglgnoredDuringExecution' to target the specific node and request 'nvidia.com/gpu: 2 in the pod's resource requests.
C. All of the above would work.
D. Use a combination of 'nodeselector' and 'tolerations' to target the specific node, and request 'nvidia.com/gpu: 2' in the pod's resource requests.
E. Use 'kubectl patch node -p to make the node only schedulable for your pod, and then request 'nvidia.com/gpu: 2' in the pod's resource requests.

Answer: B

Explanation:
Using 'nodeAffinity' with 'requiredDuringSchedulinglgnoredDuringExecution' ensures that the pod is scheduled on the specific node. nodeSelector' only allows simple label matching. Setting the node to unschedulable is overly restrictive, and the 'tolerations' approach (D) is for nodes with taints, which is not the goal here. 'requiredDuringSchedulinglgnoredDuringExecution' is prefered, it allows pod to continue if nodes go down after schduling , 'requiredDuringSchedulingRequiredDuringExecution' will cause the pod to be evicted

NEW QUESTION # 39
What is the primary purpose of assigning a provisioning role to a node in NVIDIA Base Command Manager (BCM)?

A. To assign the node as a storage manager for certified storage
B. To enable the node to monitor GPU utilization across the cluster
C. To allow the node to manage software images and provision other nodes
D. To configure the node as a container orchestration manager

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In NVIDIA Base Command Manager (BCM), assigning theprovisioning roleto a node enables that node to manage software images and perform provisioning tasks for other nodes in the cluster. This role allows automated deployment and configuration of cluster nodes, ensuring consistency and simplifying large-scale management. It is not primarily responsible for container orchestration, GPU monitoring, or storage management.

NEW QUESTION # 40
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