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You are managing an on-premises cluster using NVIDIA Base Command Manager (BCM) and need to extend your computational resources into AWS when your local infrastructure reaches peak capacity.
What is the most effective way to configure cloudbursting in this scenario?
A. Use BCM's built-in load balancer to distribute workloads evenly between on-premises and cloud resources without any pre-configuration.
B. Manually provision additional cloud nodes in AWS when the on-premises cluster reaches its limit.
C. Use BCM's Cluster Extension feature to automatically provision AWS resources when local resources are exhausted.
D. Set up a standby deployment in AWS and manually switch workloads to the cloud during peak times.
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIA Base Command Manager (BCM) provides aCluster Extension featurethat enables automatic provisioning and scaling of cloud resources (e.g., AWS) when on-premises capacity is fully utilized. This cloudbursting capability allows seamless extension of computational resources without manual intervention, improving flexibility and reducing downtime during peak demand. Options A, B, and C involve manual or incomplete automation approaches that do not leverage BCM's integrated cluster extension functionality.
NEW QUESTION # 51
You're trying to build a Docker image that includes NCCL for multi-GPU training. You've installed NCCL using 'apt-get' , but when you run the container, you get errors indicating that NCCL cannot find the GPUs. What's the MOST likely problem?
A. You haven't set the 'NCCL_DEBUG' environment variable to 'INFO' or a higher level for debugging. Set this variable to get more verbose NCCL output.
B. You did not explicitly install CUDA development headers, which are necessary for NCCL to function properly. Add the 'cuda-nvcc' package to your 'apt-get install' command.
C. The NCCL version is incompatible with the CUDA driver version. Verify compatibility and install a compatible version of NCCL.
D. The network configuration within the container is preventing NCCL from communicating between GPUs. Ensure proper network setup and firewall rules.
E. The Docker container is not configured to use the NVIDIA runtime, so NCCL cannot access the GPUs. Configure the Docker daemon correctly.
Answer: D,E
Explanation:
If NCCL can't find the GPUs, the Docker container is not configured with NVIDIA runtime which allows the container to detect the GPUs. Also, NCCL depends on network between GPUs for multi-GPU operation. Network errors can also cause the issue.
NEW QUESTION # 52
A fleet of edge devices running AI inference applications experiences intermittent network connectivity. You need to configure Fleet Command to handle these disruptions gracefully. Which of the following actions should you take to ensure application resilience?
A. Increase the timeout values for all Fleet Command operations.
B. Implement a local caching mechanism on the edge devices to store inference results during network outages and synchronize them when connectivity is restored.
C. Disable all updates to the edge devices during periods of network instability.
D. Configure Fleet Command to immediately roll back deployments when network connectivity is lost.
E. Instruct users to manually restart applications on the edge devices after network outages.
Answer: B
Explanation:
A local caching mechanism allows edge devices to continue operating during network disruptions, ensuring application resilience. Rolling back deployments (A) is disruptive. Disabling updates (C) prevents improvements. Increasing timeouts (D) might help with transient issues but doesn't address the underlying problem. Manual restarts (E) are not scalable or reliable.
NEW QUESTION # 53
You are encountering issues with a DOCA application failing to initialize on a BlueField-3 DPU. The error logs indicate a problem with allocating memory. Which of the following steps would be most effective in diagnosing the root cause?
A. Reinstall the DOCA SDK on the DPU to ensure that all memory management libraries are correctly installed.
B. Verify that the DOCA application is requesting memory from the appropriate memory domain (e.g., DOCA MD) and that sufficient memory is available in that domain.
C. Check the DPU's memory usage using tools like 'free -m' or 'top' to identify potential memory exhaustion.
D. Examine the DOCA application code for potential memory leaks or incorrect memory allocation sizes.
E. Disable SELinux on the DPU to eliminate potential permission issues affecting memory allocation.
Answer: B,C,D
Explanation:
Memory allocation issues can stem from various causes, including memory exhaustion on the DPU, memory leaks in the application code, or insufficient memory within the configured memory domain. Therefore, checking memory usage, examining the code, and verifying memory domain configurations are crucial steps in diagnosing the problem.
NEW QUESTION # 54
You need to configure network settings for your Fleet Command deployment. You want to ensure that edge devices can only communicate with the Fleet Command server over a specific port and protocol for security reasons. Which of the following configurations is the MOST appropriate?
A. Disable all network access on the edge devices except for SSH.
B. Rely on the default network settings provided by the operating system.
C. Open all ports on the edge devices and the Fleet Command server to allow unrestricted communication.
D. Configure a VPN for all communication, even local communication.
E. Configure a firewall on the edge devices and the Fleet Command server to allow communication only on the designated port and protocol (e.g., HTTPS on port 443),
Answer: E
Explanation:
A firewall provides the necessary security by restricting communication to only the required port and protocol. Opening all ports (A) is insecure. Disabling network access (C) prevents functionality. Relying on defaults (D) is insufficient. VPN is not needed for local communication and overcomplicated. (E)
NEW QUESTION # 55
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