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| Section | Objectives |
|---|---|
| AI Operations Fundamentals | - Core concepts of MLOps and LLMOps - Introduction to AI systems lifecycle in production |
| Security and Governance | - Compliance and governance in AI systems - Data privacy and secure model deployment |
| Model Deployment and Serving | - Inference serving frameworks and APIs - Model packaging and containerization |
| Infrastructure for AI Workloads | - Cloud and on-prem AI deployment architectures - GPU-accelerated computing environments |
| Monitoring and Observability | - Drift detection and alerting - Performance monitoring for AI models |
| Optimization and Lifecycle Management | - Model optimization techniques (quantization, pruning) - Continuous training and deployment pipelines |
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NEW QUESTION # 53
You are observing intermittent failures in your NVSHMEM application, and you suspect memory corruption. What is a good first step to debug this issue using NVSHMEM's debugging tools?
Answer: D
Explanation:
Setting enables detailed memory allocation and deallocation tracing within NVSHMEM, which can help identify memory corruption issues. NCCL DEBUG is for NCCL issues, not NVSHMEM. Scuda-memcheck' is a good general tool for CUDA memory errors, but 'NVSHMEM_DEBUG' is more specific to NVSHMEM's managed memory. 'valgrind' is a general-purpose memory debugger, but NVSHMEM's built-in tracing is usually more effective for NVSHMEM-specific problems. The 'ulimit' value affects resource limits, but it doesn't directly help debug memory corruption.
NEW QUESTION # 54
A Fleet Command system administrator wants to create an organization user that will have the following rights:
For locations - read only
For Applications - read/write/admin
For Deployments - read/write/admin
For Dashboards - read only
What role should the system administrator assign to this user?
Answer: B
Explanation:
The Fleet Command Operator role is designed to provide users with read-only access to locations and dashboards while granting full read/write/admin rights for applications and deployments. This matches the described access requirements where the user can manage applications and deployments but only view locations and dashboards without modification rights.
Other roles like Fleet Command Admin have broader permissions, Supporter has more limited access, and Viewer is primarily read-only for all resources.
NEW QUESTION # 55
Your BCM pipeline uses a custom CUDA kernel. After upgrading the NVIDIA driver, the kernel fails to compile with an obscure error.
What is the MOST likely cause and how do you resolve it?
Answer: A
Explanation:
Driver upgrades can introduce CUDA toolkit incompatibilities or changes in the CUDAAPI that necessitate recompilation or code modification.
NEW QUESTION # 56
You are using a custom topology with NVSwitches, and 'nvsm' is not detecting the correct links. You need to manually define the topology. Which of the following is the correct way to provide a custom topology definition to
Answer: E
Explanation:
While the exact file path might vary slightly depending on the distribution and configuration, the standard approach is to define custom topologies in a dedicated topology file (e.g., S/etc/nvsm/nvsm.topology'). The other options are either incorrect or related to other services. 'nvsm' reads the 'nvsm.topology' file at startup.
NEW QUESTION # 57
You have noticed that users can access all GPUs on a node even when they request only one GPU in their job script using --gres=gpu:1. This is causing resource contention and inefficient GPU usage.
What configuration change would you make to restrict users' access to only their allocated GPUs?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
To restrict users' access strictly to the GPUs allocated to their jobs, Slurm usescgroups (control groups)for resource isolation. Enabling device cgroup enforcement by settingConstrainDevices=yesincgroup.
confenforces device access restrictions, ensuring jobs cannot access GPUs beyond those assigned.
* Increasing memory allocation or setting job priorities does not restrict device access.
* Modifying job scripts to request additional CPU cores does not limit GPU access.
Hence, enablingcgroup enforcement with ConstrainDevices=yesis the correct method to prevent users from accessing unallocated GPUs.
NEW QUESTION # 58
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