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| Section | Weight | Objectives |
|---|---|---|
| Management | 28% | - GPU cluster and compute node management - Resource allocation and scheduling - Access control and security policies - Software and container lifecycle management |
| Workload Management | 20% | - AI workload deployment and scaling - Framework and runtime configuration - Data pipeline and storage integration - Job scheduling and queue management |
| Installation and Deployment | 32% | - Container orchestration and resource management - AI cluster setup and configuration - NVIDIA software stack deployment - Driver and firmware installation |
| Troubleshooting and Optimization | 20% | - Fault diagnosis and resolution - System health and reliability maintenance - Throughput and latency optimization - Performance monitoring and analysis |
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NEW QUESTION # 38
A system administrator of a high-performance computing (HPC) cluster that uses an InfiniBand fabric for high-speed interconnects between nodes received reports from researchers that they are experiencing unusually slow data transfer rates between two specific compute nodes. The system administrator needs to ensure the path between these two nodes is optimal.
What command should be used?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To verify the optimal communication path and diagnose issues between two nodes in an InfiniBand fabric, theibtracertcommand is used. It traces the route that InfiniBand packets take through the fabric, identifying each hop and any potential bottlenecks or faulty links along the path.
* ibstatusprovides status information about local InfiniBand devices and ports.
* ibpingtests connectivity and latency between nodes.
* ibnetdiscoverdiscovers and prints the topology of the InfiniBand fabric but does not trace specific paths.
Therefore,ibtracertis the appropriate tool for path optimization verification between two compute nodes.
NEW QUESTION # 39
A system administrator is troubleshooting a Docker container that crashes unexpectedly due to a segmentation fault. They want to generate and analyze core dumps to identify the root cause of the crash.
Why would generating core dumps be a critical step in troubleshooting this issue?
Answer: A
Explanation:
Core dumps capture the memory state of a process at the time of its crash, providing a snapshot useful for post-mortem debugging. Analyzing core dumps helps identify the cause of segmentation faults or other critical errors by revealing what the process was doing at failure, including stack traces, variable states, and memory content.
NEW QUESTION # 40
A data scientist has provided you with a Jupyter Notebook running inside an NGC container. This notebook relies on a large dataset stored in an object storage service (e.g., AWS S3, Google Cloud Storage). What's the most efficient and secure way to provide the notebook access to this data without embedding credentials directly into the notebook or container image?
Answer: A,E
Explanation:
C and E are the most secure and efficient. Kubernetes Secrets allow for secure storage and management of sensitive data, which can be mounted into the container as files. Leveraging IAM roles or Service Accounts allows the container to inherit permissions from the Kubernetes cluster, eliminating the need for explicit credentials. Option B is less secure as environment variables can be easily exposed. Option A can introduce performance bottlenecks. Option D is highly discouraged due to security risks and lack of flexibility.
NEW QUESTION # 41
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?
Answer: A
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 # 42
You are deploying a DOCA application on a BlueField-3 DPU. Which of the following components are essential for enabling RDMA communication between the DPU and the host server?
Answer: B,D
Explanation:
RDMA communication requires the correct drivers (MLNX_OFED) on both ends and proper PCI passthrough or SR-IOV configuration on the host to expose the DPU's RDMA capabilities. DOCA SDK helps build the applications, firewall rules are orthogonal and DPDK is one of the option. Kernel bypass on both host and dpu is needed.
NEW QUESTION # 43
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