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NVIDIA AI Infrastructure Sample Questions:

1. You are using the NVIDIA Container Toolkit in a Kubernetes environment with multiple GPUs per node. You want to ensure that pods can request specific GPUs on a node, rather than simply requesting 'any' GPU. Which Kubernetes feature, in conjunction with the NVIDIA Device Plugin, allows you to achieve this fine-grained GPU resource allocation?

A) Topology Manager
B) Resource Quotas
C) Node Affinity
D) Taints and Tolerations
E) Device Plugins API

2. You are managing a cluster of servers with NVIDIA GPUs. You want to automate the process of installing and updating NVIDIA drivers across all nodes. Which of the following approaches would be the most scalable and maintainable?

A) Distribute the driver installation package via NFS and have each server run the installation script locally.
B) Manually SSH into each server and run the driver installation commands.
C) Use NVIDIA's container toolkit to deploy driver containers to each node.
D) Create a custom script that uses 'pdsh' or 'parallel' to execute driver installation commands on all servers simultaneously.
E) Use a configuration management tool like Ansible, Chef, or Puppet to automate the driver installation and update process.

3. You're managing a cluster of servers with BlueField-2 DPUs. One server is experiencing intermittent network connectivity issues. You suspect a problem with the DPU's firmware. Which of the following is the MOST reliable method to determine the CURRENT firmware version of the BlueField-2 DPIJ?

A) Query the DPIJ's BMC (Baseboard Management Controller) via IPMI or Redfish.
B) Run 'ethtool -i on a network interface associated with the DPIJ.
C) Examine the '/proc/driver/mlx4_core/versiorf file.
D) Use the 'mst status' command to query the device status and firmware version.
E) Check the system logs for firmware-related messages during boot.

4. In a data center utilizing NVIDIA GPUs and NVLink, what is the primary advantage of using a direct-attached NVLink network topology compared to routing traffic over the network?

A) Reduced cost of network infrastructure
B) Simplified network configuration
C) Increased network security
D) Improved power efficiency
E) Higher bandwidth and lower latency between GPUs

5. You are designing a large-scale AI training cluster spanning multiple racks. The networking topology necessitates both short-reach (within rack) and long-reach (inter-rack) connections. Which combination of cable types and transceivers is MOST cost-effective and suitable for this scenario, assuming a mix of 200GbE and 400GbE links?

A) DAC cables for all intra-rack connections and AOC cables for all inter-rack connections.
B) Passive copper cables for all intra-rack connections and ER4 transceivers with single-mode fiber for all inter-rack connections.
C) DAC cables for all intra-rack connections, and a mix of SR4 and LR4 transceivers with multimode and single-mode fiber, respectively, for inter-rack connections.
D) AOC cables for all connections, both intra-rack and inter-rack.
E) DAC cables for all intra-rack connections and FR4 transceivers with single-mode fiber for inter-rack connections.

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