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| Section | Weight | Objectives |
|---|---|---|
| Troubleshooting and Optimization | 20% | - System health and reliability maintenance - Performance monitoring and analysis - Throughput and latency optimization - Fault diagnosis and resolution |
| Installation and Deployment | 32% | - AI cluster setup and configuration - Container orchestration and resource management - Driver and firmware installation - NVIDIA software stack deployment |
| Workload Management | 20% | - Data pipeline and storage integration - AI workload deployment and scaling - Framework and runtime configuration - Job scheduling and queue management |
| Management | 28% | - Software and container lifecycle management - GPU cluster and compute node management - Resource allocation and scheduling - Access control and security policies |
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NEW QUESTION # 47
You are managing a Slurm cluster with multiple GPU nodes, each equipped with different types of GPUs.
Some jobs are being allocated GPUs that should be reserved for other purposes, such as display rendering.
How would you ensure that only the intended GPUs are allocated to jobs?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Slurm GPU resource management, thegres.conffile defines the available GPUs (generic resources) per node, whileslurm.confconfigures the cluster-wide GPU scheduling policies. To prevent jobs from using GPUs reserved for other purposes (e.g., display rendering GPUs), administrators must ensure that only the GPUs intended for compute workloads are listed in these configuration files.
* Properly configuringgres.confallows Slurm to recognize and expose only those GPUs meant for jobs.
* slurm.confmust be aligned to exclude or restrict unconfigured GPUs.
* Manual GPU assignment usingnvidia-smiis not scalable or integrated with Slurm scheduling.
* Reinstalling drivers or increasing GPU requests does not solve resource exclusion.
Thus, the correct approach is to verify and configure GPU listings accurately ingres.confandslurm.confto restrict job allocations to intended GPUs.
NEW QUESTION # 48
A data science team is using Fleet Command to deploy AI models to edge devices in a smart city project. They've noticed that some devices are consistently failing to update due to insufficient disk space. Which of the following is the MOST effective strategy to mitigate this issue?
Answer: A
Explanation:
Optimizing models (B) is helpful, but a cleanup process (E) addresses the root cause. Increasing disk space (A) might not be feasible or cost-effective. Ignoring devices (C) is unacceptable. Rolling back updates (D) is a temporary solution. Thus, automatically cleaning up unused files is the most proactive and sustainable approach.
NEW QUESTION # 49
You're setting up a Kubernetes cluster on NVIDIA DGX servers using Bare Metal Container (BCM). During the pre-flight checks, the 'kubelet' fails to start on one of the worker nodes. The logs indicate a problem with device plugin registration. Which of the following is the MOST likely cause and the best initial troubleshooting step?
Answer: B
Explanation:
The NVIDIA Container Toolkit is essential for exposing GPU devices to containers within Kubernetes. A missing or misconfigured toolkit is the most common reason for device plugin registration failures. Checking its installation and configuration is the crucial first step. Incorrect driver version (A) could be an issue but less likely. Firewall (B) and SELinux (C) are also possibilities, but Toolkit (D) is most direct. CPU resources (E) are unlikely to cause device registration issues.
NEW QUESTION # 50
You are tasked with setting up BCM in a highly secure environment that has very restrictive internet access for the worker nodes in your kubernetes cluster. What steps should you take to ensure a successful kubernetes installation, keeping the environment limitations in mind?
Answer: A
Explanation:
In environments with restrictive internet access, setting up local mirrors is the standard practice. Setting up local mirrors of images and packages avoids security risks associated with using external proxy servers and is a controlled setup of dependencies. Requesting for exceptions may not be viable in a secure environment, and directly instructing containerized applications to download dependencies does not follow kubernetes setup or BCM. Manually distributing packages also increases the risks involved.
NEW QUESTION # 51
Which deployment strategy allows two versions of a model to run simultaneously and gradually shifts traffic from the old version to the new version to minimize risk during updates?
Answer: C
Explanation:
Canary deployment introduces a new model version to a small subset of users first. Traffic is gradually increased if performance is stable. This reduces risk by allowing early detection of issues before full-scale rollout in production environments.
NEW QUESTION # 52
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