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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Management | 28% | - GPU cluster and compute node management - Access control and security policies - Software and container lifecycle management - Resource allocation and scheduling |
| Topic 2: Installation and Deployment | 32% | - NVIDIA software stack deployment - Driver and firmware installation - Container orchestration and resource management - AI cluster setup and configuration |
| Topic 3: Workload Management | 20% | - Job scheduling and queue management - AI workload deployment and scaling - Framework and runtime configuration - Data pipeline and storage integration |
| Topic 4: Troubleshooting and Optimization | 20% | - Throughput and latency optimization - Fault diagnosis and resolution - System health and reliability maintenance - Performance monitoring and analysis |
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NEW QUESTION # 73
You have configured MIG instances for different users in a multi-tenant environment. One user complains that their application is running slower than expected, despite having a dedicated MIG instance. You suspect resource contention on the host system. Which of the following could be causing the slowdown, even with MIG in place?
Answer: B,D,E
Explanation:
MIG provides GPU resource isolation, but it does not isolate other system resources. CPU oversubscription, insufficient host memory, and network bandwidth limitations can all contribute to performance slowdowns, even with dedicated MIG instances. It's important to monitor and manage these resources in addition to GPU resources.
NEW QUESTION # 74
A system administrator needs to scale a Kubernetes Job to 4 replicas.
What command should be used?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The correct command to scale a Kubernetes Job to a specific number of replicas iskubectl scale job -- replicas=4. This explicitly sets the number of desired pod instances for the Job resource. The other commands are either invalid (stretch), apply to Deployments rather than Jobs (autoscale deployment), or use incorrect syntax (-r).
NEW QUESTION # 75
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: B
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 # 76
You're using BCM to manage a cluster and need to upgrade the Kubernetes version. What considerations are critical to ensure a smooth upgrade process?
Answer: A,B,C,D
Explanation:
Backing up etcd is crucial for rollback. Resource sufficiency prevents upgrade failures. Testing in staging identifies potential issues. Draining minimizes downtime. NVIDIA drivers should ideally be checked for compatibility and potentially updated before the Kubernetes upgrade.
NEW QUESTION # 77
What two (2) platforms should be used with Fabric Manager? (Choose two.)
Answer: B,C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIA Fabric Manager is designed to manage and optimize fabric resources like NVLink and NVSwitch in enterprise-class platforms such as HGX and DGX systems. These platforms have the necessary hardware fabric components. The L40S Certified and GeForce series are either not compatible or do not require Fabric Manager.
NEW QUESTION # 78
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