NCP-AIO by NVIDIA Actual Free Exam Questions And Answers

Question 1

Which approach involves running a new model in parallel with the existing production model without affecting user-facing predictions to evaluate its performance in real-time conditions?

A. Canary testing

B. Shadow deployment

C. A/B testing

D. Blue-green deployment

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Correct Answer: B Vote an answer

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Question 2

You are an administrator managing a large-scale Kubernetes-based GPU cluster using Run:AI.
To automate repetitive administrative tasks and efficiently manage resources across multiple nodes, which of the following is essential when using the Run:AI Administrator CLI for environments where automation or scripting is required?

A. Use the CLI to manually allocate specific GPUs to individual jobs for better resource management.

B. Install the CLI on Windows machines to take advantage of its scripting capabilities.

C. Ensure that the Kubernetes configuration file is set up with cluster administrative rights before using the CLI.

D. Use the runai-adm command to directly update Kubernetes nodes without requiring kubectl.

Discussion 0

Correct Answer: C Vote an answer

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Question 3

A system administrator needs to optimize the delivery of their AI applications to the edge.
What NVIDIA platform should be used?

A. Base Command Manager

B. Base Command Platform

C. Fleet Command

D. NetQ

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Correct Answer: C Vote an answer

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Question 4

You are managing a high availability (HA) cluster that hosts mission-critical applications. One of the nodes in the cluster has failed, but the application remains available to users.
What mechanism is responsible for ensuring that the workload continues to run without interruption?

A. Manual intervention by the system administrator to restart services.

B. Load balancing across all nodes in the cluster.

C. The failover mechanism that automatically transfers workloads to a standby node.

D. Data replication between nodes to ensure data integrity.

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Correct Answer: C Vote an answer

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Question 5

After completing the installation of a Kubernetes cluster on your NVIDIA DGX systems using BCM, how can you verify that all worker nodes are properly registered and ready?

A. Run kubectl get nodes to verify that all worker nodes show a status of "Ready".

B. Check each node manually by logging in via SSH and verifying system status with systemctl.

C. Run kubectl get pods to check if all worker pods are running as expected.

Discussion 0

Correct Answer: A Vote an answer

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Question 6

What is the primary goal of observability in AI operations when monitoring machine learning systems deployed in production environments?

A. Reduce model size

B. Provide system transparency

C. Increase training speed

D. Improve data labeling

Discussion 0

Correct Answer: B Vote an answer

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Question 7

What is the primary purpose of assigning a provisioning role to a node in NVIDIA Base Command Manager (BCM)?

A. To assign the node as a storage manager for certified storage

B. To enable the node to monitor GPU utilization across the cluster

C. To allow the node to manage software images and provision other nodes

D. To configure the node as a container orchestration manager

Discussion 0

Correct Answer: C Vote an answer

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Question 8

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 disable other logging mechanisms, reducing noise in the output.

B. Debugging logs prevent the container from being removed after it stops, allowing for easier inspection.

C. Debugging logs provide detailed insights into the Docker daemon's internal operations.

D. Debugging logs fix issues related to container performance and resource allocation.

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Correct Answer: C Vote an answer

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Question 9

You are managing a deep learning workload on a Slurm cluster with multiple GPU nodes, but you notice that jobs requesting multiple GPUs are waiting for long periods even though there are available resources on some nodes.
How would you optimize job scheduling for multi-GPU workloads?

A. Reduce memory allocation per job so more jobs can run concurrently, freeing up resources faster for multi-GPU workloads.

B. Increase time limits for smaller jobs so they don't interfere with multi-GPU job scheduling.

C. Set up separate partitions for single-GPU and multi-GPU jobs to avoid resource conflicts between them.

D. Ensure that job scripts use --gres=gpu:<number> and configure Slurm's backfill scheduler to prioritize multi-GPU jobs efficiently.

Discussion 0

Correct Answer: D Vote an answer

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