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NVIDIA NCP-AII Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Security and Best Practices	10-15%	- Compliance considerations - Security fundamentals - Operational best practices
Topic 2: AI Infrastructure Fundamentals	15-20%	- AI and Deep Learning concepts - GPU architecture basics - NVIDIA software stack overview - Data center infrastructure requirements
Topic 3: Monitoring and Management	15-20%	- Resource utilization - Performance monitoring - Troubleshooting basics - NVIDIA management tools
Topic 4: NVIDIA AI Infrastructure Components	25-30%	- Storage solutions for AI workloads - NVIDIA AI Enterprise software - NVIDIA DGX systems - NVIDIA networking solutions (Mellanox)
Topic 5: Deployment and Configuration	25-30%	- Cluster configuration - Software deployment - System installation and setup - Network configuration

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NVIDIA AI Infrastructure Sample Questions (Q69-Q74):

NEW QUESTION # 69
Which of the following storage technologies are most suitable for storing large training datasets used in deep learning, considering both performance and cost?

A. Tape backup systems
B. SATA HDDs in a network-attached storage (NAS) configuration
C. A parallel file system (e.g., BeeGFS, Lustre) deployed on NVMe SSDs
D. High-performance NVMe SSDs in a local RAID configuration
E. Object storage (e.g., AWS S3, Azure Blob Storage) accessed directly from the training nodes

Answer: C

Explanation:
NVMe SSDs in a local RAID offer high performance and relatively low latency, making them suitable for data that needs to be accessed quickly. Parallel file systems deployed on NVMe SSDs provide the highest performance and scalability, especially for large datasets accessed concurrently by multiple training nodes. Object storage can be used for initial data ingest or archival but is generally slower than local or parallel file systems for training. SATA HDDs and Tape backup systems are a low performing option for this case.

NEW QUESTION # 70
You are installing the NGC CLI using 'pip' behind a corporate proxy. The installation fails due to connection errors. How do you configure pip' to use the proxy during the NGC CLI installation?

A. Use the '-proxy option with the 'pip install' command, specifying the proxy server address and port.
B. NGC CLI does not support the use of proxies
C. Download the NGC CLI package manually and install it offline.
D. Set the 'http_proxy' and 'https_proxy' environment variables with the proxy server address and port.
E. Create a 'pip.conf file in the appropriate directory (e.g., '-/.pip/pip.conf) and configure the proxy settings within the file.

Answer: A,D,E

Explanation:
You can configure 'pip' to use a proxy by setting environment variables (A), using the '-proxy' option with the 'pip install' command (B), or creating a 'pip.conf file (C). Downloading the package manually (D) is a workaround for the initial installation but doesn't address ongoing communication with NGC. Option E is incorrect since NGC CLI does support proxy configuration.

NEW QUESTION # 71
While running a large A1 training job, you observe the following output from 'nvidia-smi':
GPU O: P2
GPU 1: P2
GPU 2: P2
GPU 3: P2
What does the 'P2' state indicate, and what steps should you take to investigate this further in the context of validating optimal hardware operation?

A. P2 indicates the GPU is running at maximum clock speed. Check for thermal throttling.
B. P2 indicates the GPUs are in a high-performance state; no further investigation is needed.
C. P2 indicates a critical error state; immediately halt the training job and check the system logs for hardware failures.
D. P2 indicates the GPUs are in a low-power idle state; investigate if the driver is correctly configured and the workload is properly utilizing the GPUs.

Answer: D

Explanation:
P2 typically indicates a power-saving state where the GPU is operating at a reduced clock speed. It's crucial to investigate whether the workload is demanding sufficient resources from the GPUs, and whether power limits or other configuration settings are preventing the GPUs from reaching their maximum performance state. Review 'nvidia-smi -q' output for power usage and clock speeds to verify proper operation. Other power states (PO, Pl) represent varying levels of performance.

NEW QUESTION # 72
A data scientist needs to run a Jupyter notebook on specific GPU cards. What command should be used?

A. docker run --runtime=nvidia -dpu "1,2" -p 8888:8888 jupyter/scipy-notebook:2023-09-25
B. docker run --gpus I"device=UUID-ABCDEF,1" -p 8888:8888 jupyter/scipy-notebook:2023-09-25
C. docker run --gpus all --limit-gpu ""device=UUID-ABCDEF" -p 8888:8888 jupyter/scipy- notebook:2023-09-25
D. docker run --dpu "1,2" -p 8888:8888 jupyter/scipy-notebook:2023-09-25

Answer: B

Explanation:
Docker uses the --gpus flag with a device= selector to expose only specific GPUs to a container.
The selector can target GPUs by UUID and index, allowing the Jupyter notebook container to run only on the specified GPU cards while publishing port 8888 for notebook access.

NEW QUESTION # 73
When installing a GPU driver on a Linux system that already has a previous driver version installed, what is the recommended procedure to ensure a clean and stable installation?

A. Blacklist the nouveau driver, download the CUDA toolkit, and run the installation script with default options.
B. Run 'nvidia-uninstall' if it exists, otherwise manually remove the NVIDIA kernel modules and libraries from '/lib/modules' and '/usr/lib'.
C. Purge the existing NVIDIA driver packages using 'apt purge nvidia- or 'yum remove nvidia- s, reboot the system, and then install the new driver package.
D. Install the new driver using the .run' file from NVIDIA's website, accepting all default options.
E. Simply install the new driver package using 'apt install' or 'yum install' without removing the old driver.

Answer: C

Explanation:
Purging the existing drivers using the package manager ensures that all related files and configurations are removed, preventing conflicts with the new driver. Rebooting after purging allows the system to load without the old drivers. While using the .run file is an option, using the package manager (if available) is generally preferred for easier management.

NEW QUESTION # 74
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