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NVIDIA AI Operations Sample Questions (Q31-Q36):

NEW QUESTION # 31
A data scientist submits a Run.ai job requesting 4 GPUs. However, due to resource constraints, only 2 GPUs are immediately available. You want the job to automatically start running as soon as the remaining 2 GPUs become available, without manual intervention. How do you configure Run.ai to achieve this?

A. Configure a lower priority for the job.
B. Set the job's 'restartPolicy' to 'Always'.
C. Set a higher quota for the team.
D. Enable gang scheduling for the job.
E. Use Run.ai's 'suspend' and 'resume' commands manually.

Answer: D

Explanation:
Gang scheduling ensures that all requested resources (in this case, all 4 GPUs) are allocated before the job starts. The job will remain in a pending state until all resources are available, and then it will automatically start. 'restartPolicy only applies if a job fails after it has already started. Lower priority would make it less likely to start. Manually suspending and resuming requires intervention. A quota impacts how much you can submit overall, not the allocation of the complete resources requested by a single job.

NEW QUESTION # 32
You are using Fleet Command to manage a fleet of edge devices. You need to collect logs from all devices for debugging purposes. Which of the following approaches is the MOST efficient and scalable?

A. Configure a centralized logging system (e.g., ELK stack or similar) and configure the edge devices to forward their logs to the central system.
B. Manually SSH into each device and copy the logs.
C. Disable logging on the edge devices to save disk space.
D. Use Fleet Command's built-in log collection features (if available) to gather logs from the devices.
E. Email the logs from each device.

Answer: A,D

Explanation:
A centralized logging system and Fleet Command's built-in features are the most scalable and efficient ways to collect logs. Manual SSH (A) is impractical. Disabling logging (D) prevents debugging. Email (E) is not scalable or secure.

NEW QUESTION # 33
You're tasked with implementing a secure and auditable deployment pipeline for AI models using Fleet Command. Which of the following methods BEST ensures that all model deployments are tracked and authorized?

A. Using a third-party CI/CD tool integrated with Fleet Command.
B. Leveraging Fleet Command's built-in deployment history and user access controls.
C. Manually documenting each deployment in a spreadsheet.
D. Relying on email notifications to track deployments.
E. Creating a custom script to log deployments to a local text file on the Fleet Command server.

Answer: B

Explanation:
Fleet Command's built-in features offer the most robust and secure way to track deployments and manage user access. Manual spreadsheets (A) are error-prone. Custom scripts (C) can be less secure and harder to maintain. Email notifications (D) lack auditability. While CI/CD tools (E) can be integrated, leveraging Fleet Command's native capabilities is the most straightforward and secure option.

NEW QUESTION # 34
You are tasked with deploying a DOCA service on an NVIDIA BlueField DPU in an air-gapped data center environment. The DPU has the required BlueField OS version (3.9.0 or higher) installed, and you have access to the necessary container image from NVIDIA's NGC catalog. However, you need to ensure that the deployment process is successful without an internet connection.
Which of the following steps should you take to deploy the DOCA service on the DPU?

A. Use the host system's Docker engine to pull the container image and deploy it on the DPU via SSH.
B. Install Docker on the DPU, pull the container directly from NGC, and run it using 'docker run' with appropriate environment variables.
C. Pull the container image from NGC using Docker and modify the YAML file before deployment.
D. Manually download the container image and YAML file beforehand, transfer them to the DPU, and deploy using Kubernetes with standalone Kubelet.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In an air-gapped environment where the DPU has no internet connectivity, direct pulling of container images from NVIDIA's NGC catalog is not possible. The recommended approach is tomanually download the required container image and YAML deployment filesfrom a connected system, then transfer these files to the DPU. Deployment is then performed using Kubernetes with a standalone Kubelet on the DPU, which can deploy the preloaded container image offline. This ensures the deployment proceeds successfully without internet access.

NEW QUESTION # 35
An AI data center is experiencing inconsistent training performance. After investigation, it's determined that storage I/O is the bottleneck. Which of the following actions can help mitigate the issue?

A. Optimize data loading and pre-processing pipelines to reduce I/O operations.
B. Migrate the data to a faster storage tier (e.g., from HDD to SSD).
C. Reduce the number of GPUs used for training.
D. Compress the data more aggressively.
E. Increase the network bandwidth between compute nodes and the storage system.

Answer: A,B,E

Explanation:
Increasing network bandwidth ensures data can be transferred faster. Migrating to a faster storage tier improves I/O performance directly. Optimizing data pipelines reduces the overall amount of data that needs to be read/written. Reducing GPUs will not solve I/O bottleneck, just slow down training. Aggressive compression can add overhead.

NEW QUESTION # 36
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