Practice Free NCP-AII Exam Online Questions
Question #111
When updating the firmware on an NVLink switch transceiver, how can an engineer apply new firmware without interrupting the network?
- A . mlxfwreset -d -lid 27 reset –yes to reset the transceiver
- B . Physically disconnect and reconnect the transceiver.
- C . flint -d -lid 27 –linkx –linkx_auto_update –activate
- D . nv action reboot system to force immediate activation.
Correct Answer: C
C
Explanation:
NVIDIA’s LinkX optical transceivers and active copper cables often require firmware updates to ensure compatibility and performance optimizations. In a production DGX SuperPOD environment, interrupting the NVLink fabric can cause GPU-to-GPU communication failures and crash training jobs. To mitigate this, NVIDIA utilizes the flint utility (part of MFT) with specific flags for "Live" or "Seamless" updates. The –linkx flag targets the transceiver or cable specifically, rather than the switch ASIC itself. The –linkx_auto_update flag automates the sequence, while the –activate flag ensures the new firmware is applied to the module’s active memory without requiring a full system reboot or a manual flap of the network link. This "in-service" update capability is essential for large-scale AI clusters where uptime is measured in weeks or months of continuous training. By using the – lid (Logical Identifier) target, an administrator can address specific modules across the fabric from a central management node, ensuring that the high-bandwidth NVLink mesh remains stable while maintaining the latest hardware optimizations.
C
Explanation:
NVIDIA’s LinkX optical transceivers and active copper cables often require firmware updates to ensure compatibility and performance optimizations. In a production DGX SuperPOD environment, interrupting the NVLink fabric can cause GPU-to-GPU communication failures and crash training jobs. To mitigate this, NVIDIA utilizes the flint utility (part of MFT) with specific flags for "Live" or "Seamless" updates. The –linkx flag targets the transceiver or cable specifically, rather than the switch ASIC itself. The –linkx_auto_update flag automates the sequence, while the –activate flag ensures the new firmware is applied to the module’s active memory without requiring a full system reboot or a manual flap of the network link. This "in-service" update capability is essential for large-scale AI clusters where uptime is measured in weeks or months of continuous training. By using the – lid (Logical Identifier) target, an administrator can address specific modules across the fabric from a central management node, ensuring that the high-bandwidth NVLink mesh remains stable while maintaining the latest hardware optimizations.
Question #112
You are designing an AI infrastructure using NVIDIA HGX AIOO servers. These servers support both PCle Gen4 and NVLink for GPU interconnects.
Which statement is MOST accurate concerning the choice between PCle Gen4 and NVLink for inter-GPU communication within a single HGX AIOO server for deep learning training?
- A . PCle Gen4 offers higher bandwidth and lower latency compared to NVLink, making it the preferred choice for all deep learning workloads.
- B . NVLink provides a direct, high-bandwidth, low-latency connection between GPUs, which is generally superior to PCle Gen4 for deep learning training involving frequent inter-GPU communication.
- C . The choice between PCle Gen4 and NVLink depends solely on the type of deep learning framework being used; TensorFlow requires PCle Gen4, while PyTorch benefits from NVLink.
- D . PCle Gen4 is more cost-effective and power-efficient than NVLink, making it the optimal choice for smaller AI training datasets.
- E . NVLink and PCle Gen4 offer identical performance for inter-GPU communication; the choice is arbitrary.
Correct Answer: B
B
Explanation:
NVLink is specifically designed for high-bandwidth, low-latency communication between GPUs, making it superior to PCle Gen4 for deep learning training where GPUs frequently exchange data. NVLink allows GPIJs to share memory directly.
B
Explanation:
NVLink is specifically designed for high-bandwidth, low-latency communication between GPUs, making it superior to PCle Gen4 for deep learning training where GPUs frequently exchange data. NVLink allows GPIJs to share memory directly.
Question #113
When installing multiple NVIDIA GPUs, which of the following factors are MOST important to consider regarding PCIe slot configuration? (Choose two)
- A . Ensure all GPUs are installed in slots of the same color.
- B . Ensure each GPU is installed in a slot with sufficient PCIe lanes (e.g., x16).
- C . Ensure the PCIe slots are directly connected to the CPU for optimal bandwidth.
- D . Install the GPUs in the lowest numbered slots first.
- E . Ensure all GPUs have the same PCIe generation (e.g. Gen4).
Correct Answer: B,C
B,C
Explanation:
The number of PCIe lanes directly impacts bandwidth. Direct CPU connection minimizes latency. Slot color and numbering are usually irrelevant. Same PCIe Gen isn’t critical as long as minimum requirements are met.
B,C
Explanation:
The number of PCIe lanes directly impacts bandwidth. Direct CPU connection minimizes latency. Slot color and numbering are usually irrelevant. Same PCIe Gen isn’t critical as long as minimum requirements are met.
Question #114
Which of the following storage technologies are most suitable for storing large training datasets used in deep learning, considering both performance and cost?
- A . High-performance NVMe SSDs in a local RAID configuration
- B . SATA HDDs in a network-attached storage (NAS) configuration
- C . Object storage (e.g., AWS S3, Azure Blob Storage) accessed directly from the training nodes
- D . A parallel file system (e.g., BeeGFS, Lustre) deployed on NVMe SSDs
- E . Tape backup systems
Correct Answer: D
D
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.
D
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.
Question #115
A user reports that their deep learning training job is crashing with a ‘CUDA out of memory’ error, even though ‘nvidia-smi’ shows plenty of free memory on the GPU. The job uses TensorFlow.
What are the TWO most likely causes?
- A . The TensorFlow version is incompatible with the installed NVIDIA driver.
- B . TensorFlow is allocating memory on the CPU instead of the GPU.
- C . TensorFlow is fragmenting GPU memory, making it difficult to allocate contiguous blocks.
- D . The CUDA VISIBLE DEVICES environment variable is not set correctly.
- E . The system’s swap space is full, preventing memory from being allocated.
Correct Answer: C,D
C,D
Explanation:
‘CUDA out of memory errors, despite seemingly available GPU memory, often indicate memory fragmentation or improper GPU assignment. TensorFlow can fragment GPU memory, leading to allocation failures even if sufficient total memory is available. The variable controls which GPUs TensorFlow can access. If it’s not set or is set incorrectly, TensorFlow might be trying to allocate memory on a non-existent or unavailable GPU. While TensorFlow version incompatibilities can cause issues, they are less likely to directly manifest as ‘CUDA out of memory’ errors. TensorFlow typically prioritizes GPU memory allocation if configured correctly.
C,D
Explanation:
‘CUDA out of memory errors, despite seemingly available GPU memory, often indicate memory fragmentation or improper GPU assignment. TensorFlow can fragment GPU memory, leading to allocation failures even if sufficient total memory is available. The variable controls which GPUs TensorFlow can access. If it’s not set or is set incorrectly, TensorFlow might be trying to allocate memory on a non-existent or unavailable GPU. While TensorFlow version incompatibilities can cause issues, they are less likely to directly manifest as ‘CUDA out of memory’ errors. TensorFlow typically prioritizes GPU memory allocation if configured correctly.
Question #116
You are attempting to install NGC CLI on a CentOS 7 system, but the ‘pip install nvidia-cli’ command fails with a ‘Could not find a version that satisfies the requirement nvidia-cli’ error. You have confirmed that ‘pip’ is installed and working.
What could be the cause of this issue?
- A . The CentOS 7 system does not have the required Python version installed. NGC CLI requires Python 3.6 or later.
- B . The system’s package manager (YUM) is not configured correctly, preventing ‘pip’ from finding the NGC CLI package.
- C . The ‘pip’ version is outdated and incompatible with the NGC CLI package. Upgrade ‘pip’ using ‘pip install ―upgrade pip’.
- D . The system’s firewall is blocking access to the Python Package Index (PyPl). CentOS 7 is not supported by NGC CLI.
Correct Answer: A
A
Explanation:
A likely reason is an outdated Python version (A), as NGC CLI requires Python 3.6 or later. Another potential issue is an outdated pip’ version (C) which could be incompatible with the NGC CLI package. Confirming the correct python version and up to date pip usually resolves this issue.
Option E is incorrect, CentOS 7 is supported with correct configuration.
A
Explanation:
A likely reason is an outdated Python version (A), as NGC CLI requires Python 3.6 or later. Another potential issue is an outdated pip’ version (C) which could be incompatible with the NGC CLI package. Confirming the correct python version and up to date pip usually resolves this issue.
Option E is incorrect, CentOS 7 is supported with correct configuration.