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NVIDIA AI Operations Sample Questions (Q16-Q21):

NEW QUESTION # 16
You are managing a high availability (HA) cluster that hosts mission-critical applications. One of the nodes in the cluster has failed, but the application remains available to users.
What mechanism is responsible for ensuring that the workload continues to run without interruption?

A. Manual intervention by the system administrator to restart services.
B. Data replication between nodes to ensure data integrity.
C. The failover mechanism that automatically transfers workloads to a standby node.
D. Load balancing across all nodes in the cluster.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In an HA cluster, thefailover mechanismis responsible for detecting node failures and automatically transferring workloads to a standby or redundant node to maintain service availability. This process ensures mission-critical applications continue running without interruption. Load balancing helps distribute traffic but does not handle node failures. Manual intervention is not ideal for HA, and data replication ensures data integrity but does not itself manage workload continuity.

NEW QUESTION # 17
What is the primary purpose of using a container runtime interface (CRI) with BCM and Kubernetes in an AI environment?

A. To provide a standard interface for Kubernetes to interact with different container runtimes (e.g., Docker, containerd).
B. To schedule pods onto nodes based on resource availability.
C. To handle networking for containers within the Kubernetes cluster.
D. To encrypt container images at rest and in transit.
E. To manage the lifecycle of containers (create, start, stop, delete).

Answer: A

Explanation:
The CRI allows Kubernetes to work with various container runtimes without being tightly coupled to a specific implementation. It defines an interface that container runtimes must implement. While A is true for a container runtime, the CRI is about Kubernetes interacting with it. The others are related to other parts of Kubernetes.

NEW QUESTION # 18
You have deployed the NVIDIA Device Plugin for Kubernetes on your BCM-managed cluster. After a kernel update on one of the worker nodes, the device plugin fails to discover the GPUs. The error messages indicate a mismatch between the driver version expected by the device plugin and the actual driver version installed on the node. What is the MOST reliable way to resolve this issue without disrupting other workloads?

A. Uninstall and reinstall the NVIDIA Container Toolkit on the affected worker node to automatically update the driver version.
B. Use a DaemonSet to manage the NVIDIA driver installation on all worker nodes, ensuring a consistent driver version across the cluster and compatibility with the device plugin.
C. Remove the NVIDIA Device Plugin and replace it with the 'nvidia-driver-installer' helm chart
D. Update the NVIDIA Device Plugin deployment manifest to specify the driver version installed on the node.
E. Manually downgrade the NVIDIA driver on the affected worker node to match the version expected by the device plugin.

Answer: B

Explanation:
Using a DaemonSet to manage the NVIDIA driver installation is the MOST reliable and scalable solution. It ensures that all worker nodes have the correct driver version and simplifies driver updates. Manually downgrading or updating individual nodes (A, B) is not sustainable. Reinstalling the toolkit (D) might not update the driver. Simply removing and replacing the plugin (E) doesn't address driver mismatch and would likely use a similar deployment method that would lead to the same error.

NEW QUESTION # 19
You've noticed consistently high GPU utilization but low overall throughput in your AI inference service. You suspect that a CUDA kernel is not efficiently utilizing the GPU's resources. Which profiling tool would provide the MOST detailed insights into kernel-level performance?

A. NVIDIA Nsight Systems
B. 'vmstat'
C. DCGM
D. nvidia-smi'
E. 'top'

Answer: A

Explanation:
NVIDIA Nsight Systems (and its successor Nsight Compute for kernel-level analysis) is specifically designed for profiling CUDA kernels. It provides detailed information on kernel execution time, memory access patterns, and instruction-level performance, allowing you to identify inefficiencies. 'nvidia-smr and DCGM provide high-level GPU monitoring, while 'top' and 'vmstat' are system-level tools.

NEW QUESTION # 20
You're deploying BCM on a multi-tenant Kubernetes cluster. How should you configure BCM to ensure that each tenant only has access to the GPUs allocated to their respective namespaces?

A. Manually assign GPUs to tenants using the 'nvidia-smi' command and update BCM's configuration to reflect these assignments.
B. Deploy a separate BCM instance for each tenant, each configured to manage a specific subset of GPU nodes.
C. Configure BCM to use the nodeSelector field in pod specifications to restrict jobs to specific GPUs based on tenant ownership.
D. Configure BCM with a global service account that has cluster-admin privileges.
E. Utilize Kubernetes resource quotas to limit GPU usage per namespace and configure BCM with namespace-specific service accounts with appropriate RBAC permissions.

Answer: E

Explanation:
Using Kubernetes resource quotas enforces limits on GPU usage per namespace. Configuring BCM with namespace-specific service accounts and appropriate RBAC permissions ensures that BCM only has access to resources within those namespaces. This approach provides the necessary isolation and resource management for a multi-tenant environment. A global service account would grant excessive permissions. Deploying separate BCM instances adds unnecessary complexity. NodeSelectors control pod placement but don't enforce resource quotas. Manually assigning GPUs is not scalable or manageable.

NEW QUESTION # 21
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