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NVIDIA AI Operations Sample Questions (Q34-Q39):

NEW QUESTION # 34
A system administrator is troubleshooting a Docker container that is repeatedly failing to start. They want to gather more detailed information about the issue by generating debugging logs.
Why would generating debugging logs be an important step in resolving this issue?

A. Debugging logs provide detailed insights into the Docker daemon's internal operations.
B. Debugging logs disable other logging mechanisms, reducing noise in the output.
C. Debugging logs prevent the container from being removed after it stops, allowing for easier inspection.
D. Debugging logs fix issues related to container performance and resource allocation.

Answer: A

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Generatingdebugging logsenables detailed visibility into the internal operations of the Docker daemon. These logs expose low-level errors, misconfigurations, and runtime issues that standard logs might not capture, making them essential for diagnosing why a container repeatedly fails to start.

NEW QUESTION # 35
You've deployed a container from NGC containing a computationally intensive AI model training script. You notice that the container is consistently being killed by the Kubernetes OOMKiller, even though the node has sufficient memory available. What are the possible causes and solutions?

A. The container's memory limit is set too low, causing it to exceed its allocated memory.
B. Increase the container's memory limit in the Kubernetes deployment manifest.
C. The node is running out of swap space, causing the OOMKiller to terminate processes aggressively.
D. Profile the application's memory usage to identify and fix memory leaks.
E. The application within the container has a memory leak, leading to excessive memory consumption.

Answer: A,B,D,E

Explanation:
An insufficient memory limit triggers the OOMKiller. Memory leaks cause excessive consumption. Increasing the limit and fixing leaks are solutions. C, while a potential issue in some environments, is less likely than the container-specific reasons in a Kubernetes environment.

NEW QUESTION # 36
You are deploying a multi-tenant AI platform on Kubernetes, where different teams share the same cluster. Each team should only be able to access and utilize the GPUs allocated to their respective namespaces. How can you enforce this isolation?

A. Leverage a custom admission controller to validate GPU requests and ensure they originate from authorized namespaces.
B. Use Kubernetes RBAC (Role-Based Access Control) to restrict access to GPU resources based on namespaces.
C. Configure the NVIDIA Device Plugin to only expose GPUs to pods within specific namespaces.
D. Utilize resource quotas to limit the GPU usage of each namespace, and rely on users to respect these limits.
E. Implement network policies to isolate the network traffic of different namespaces, thereby preventing unauthorized GPU access.

Answer: A,B

Explanation:
The correct answers are A and E. RBAC allows you to control who can create, modify, or delete GPU resources within each namespace. It defines what actions (verbs) are allowed on what resources. Combined with a custom admission controller to validate the GPU request, you can enforce the access control across the namespaces. Implementing an admission controller enforces policies and validates requests to access GPU resources originated from authorized namespaces. Option B doesn't exist. Option C, although helpful for network segmentation, does not directly control GPU access. Option D only limits usage, not access.

NEW QUESTION # 37
You are tasked with configuring MIG on an NVIDIAA100 GPU for a mixed AI/HPC workload. You need to create two instances: one for a deep learning training job (requiring high memory bandwidth) and another for a molecular dynamics simulation (requiring high compute throughput). Which is the MOST optimal MIG configuration to create based on these workload requirements?

A. One instance of 2g.10gb for deep learning and one instance of lg.5gb for molecular dynamics.
B. Two instances of 7g.40gb. This provides maximum performance for both workloads.
C. One instance of 3g.20gb for deep learning and one instance of 4g.20gb for molecular dynamics simulation. This configuration dedicates larger memory and compute resources to each task based on the workload.
D. Two instances of lg.5gb. This ensures balanced resource allocation.
E. One instance of 4g.20gb for deep learning and one instance of 3g.20gb for molecular dynamics.

Answer: C

Explanation:
Deep learning training typically benefits from larger memory capacities and bandwidth. While molecular dynamics often leverages compute throughput. Therefore, allocating 3g.20gb for deep learning, with focus on memory, and 4g.20gb for molecular dynamics will better utilize computational resources based on the workload characteristics. The lg,2g options are too small, and 7g option might overcommit resources that other processes or users could need on the same node.

NEW QUESTION # 38
You're using TensorFlow with TFRecords stored on a Lustre file system for a distributed training job. Despite having a high-performance Lustre setup, your training is slow. What aspect related to the TFRecords format itself can significantly impact storage performance in this scenario, and what can you do about it?

A. The Python version used by TensorFlow. Upgrade to the latest Python version.
B. The TFRecords format inherently causes metadata overhead that slows down Lustre.
C. The size of individual records within the TFRecords. Many small records will result in a lot of I/O operations. Use larger records by batching multiple samples into one record.
D. The number of TFRecord files. More files will always improve performance on Lustre.
E. The compression algorithm used for TFRecords. Uncompressed TFRecords are always faster on Lustre.

Answer: C

Explanation:
The size of the records affects Lustre performance when many small records exist. By using larger records by batching multiple samples into one record, the I/O operations get reduced therefore enhancing performance.

NEW QUESTION # 39
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