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NVIDIA AI Operations Sample Questions (Q12-Q17):
NEW QUESTION # 12
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: B
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 # 13
Your AI training pipeline involves processing large image datasets stored in a cloud object storage service (e.g., AWS S3, Google Cloud Storage). The download speed from the object storage is limiting your training performance. You are considering using caching mechanisms. Describe different caching strategies and their tradeoffs in this context.
Answer: B,C,E
Explanation:
Local SSD caching balances speed and capacity. In-memory caching offers the lowest latency but has memory limitations. Cloud provider's caching services improve performance for frequently accessed data but can have cost and complexity. Removing caching or relying solely on object storage caching is not ideal for performance-critical workloads.
NEW QUESTION # 14
An AI model training pipeline involves pre-processing large image datasets. The images are initially stored in a cost-effective object storage system. Which approach minimizes latency when transferring data from object storage to the GPUs for training?
Answer: A
Explanation:
Staging data to a high-performance parallel file system before training reduces latency by bringing the data closer to the compute nodes and providing high throughput. Directly accessing object storage introduces network latency, sharing over NFS can bottleneck, and a single SSD or HDD won't provide sufficient IOPS for multiple GPUs.
NEW QUESTION # 15
What should an administrator check if GPU-to-GPU communication is slow in a distributed system using Magnum IO?
Answer: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Slow GPU-to-GPU communication in distributed systems often relates to theconfiguration of communication libraries such as NCCL (NVIDIA Collective Communications Library) or NVSHMEM.
Ensuring these libraries are properly configured and optimized is critical for efficient GPU communication.
Limiting GPUs or increasing RAM does not directly improve communication speed, and disabling InfiniBand would degrade performance.
NEW QUESTION # 16
You want to upgrade your BCM installation to the latest version. What is the recommended approach for upgrading BCM in a production environment?
Answer: D
Explanation:
The recommended approach for upgrading BCM in a production environment involves backing up the BCM database to prevent data loss, then running the upgrade script provided with the new version, carefully following the documented upgrade procedure to ensure a smooth and consistent upgrade process. This approach minimizes downtime and reduces the risk of data corruption. The other options are either risky (e.g., overwriting the installation without backup) or involve unnecessary steps.
NEW QUESTION # 17
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