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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Installation and Deployment | 32% | - AI cluster setup and configuration - NVIDIA software stack deployment - Driver and firmware installation - Container orchestration and resource management |
| Topic 2: Workload Management | 20% | - Data pipeline and storage integration - Job scheduling and queue management - Framework and runtime configuration - AI workload deployment and scaling |
| Topic 3: Management | 28% | - Software and container lifecycle management - Resource allocation and scheduling - Access control and security policies - GPU cluster and compute node management |
| Topic 4: Troubleshooting and Optimization | 20% | - Fault diagnosis and resolution - System health and reliability maintenance - Performance monitoring and analysis - Throughput and latency optimization |
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NEW QUESTION # 27
A team is running a large distributed training job across multiple nodes in your Run.ai cluster. They are experiencing significant performance degradation due to network latency between the nodes. What are the possible solutions you can implement with Run.ai and potentially ACM to mitigate this issue?
Answer: B,E
Explanation:
RDMA is a key technology for reducing network latency in distributed training. It allows direct memory access between GPUs on different nodes, bypassing the CPU and reducing overhead. Configuring node affinity to keep the nodes within the same rack or network segment minimizes physical distance and network hops, further reducing latency. Increasing GPUs per node can help but is not directly addressing the inter- node latency issue. Data vs. model parallelism is an application-level choice. Run.ai doesn't have built-in network acceleration as a specific feature, but it supports the underlying technologies like RDMA.
NEW QUESTION # 28
You are using BCM for configuring an active-passive high availability (HA) cluster for a firewall system. To ensure seamless failover, what is one best practice related to session synchronization between the active and passive nodes?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
A best practice for active-passive HA clusters, such as for firewall systems managed via BCM, is touse a heartbeat networkto synchronize session state data between active and passive nodes. This real-time synchronization allows the passive node to take over seamlessly in case the active node fails, maintaining session continuity and minimizing downtime. Configuring different zone names or firewall models can cause incompatibility, and manual synchronization is prone to errors and delays.
NEW QUESTION # 29
Which of the following statements regarding the NVIDIA Device Plugin for Kubernetes are correct?
Answer: B,D
Explanation:
The correct answers are A and C. The NVIDIA Device Plugin discovers NVIDIA GPUs on each node and advertises them as resources to the Kubernetes scheduler. It enables Kubernetes to allocate GPUs to containers. It does not install drivers (that's a separate process). It works with the NVIDIA Container Toolkit to provide the necessary libraries within the container. It does not replace the NVIDIA Container Toolkit; they work in conjunction.
NEW QUESTION # 30
A BCM pipeline is failing with 'CUDA out of memory' errors, even though "nvidia-smi' reports available GPU memory. What steps should you take to diagnose and resolve this issue?
Answer: C
Explanation:
Reducing batch size, enabling CUDA memory pooling, and increasing shared memory allocation can all alleviate CUDA out-of- memory errors. CUDA memory pooling allows for more efficient memory reuse. Increasing shared memory can avoid allocation limits within the BCM pipeline.
NEW QUESTION # 31
You have a Slurm cluster configured with multiple partitions, and you want to restrict a specific user group to only submit jobs to a particular partition. How can you achieve this using Slurm's Quality of Service (QOS) and Access Control features?
Answer: B
Explanation:
Creating a QOS that restricts access to the desired partition and associating that QOS with the user group is the most direct and controlled method. The 'sacctmgr' tool is used to manage QOS and user/group associations.
NEW QUESTION # 32
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