Question 1

Which of the following best describes how RDMA (Remote Direct Memory Access) operations work within the InfiniBand protocol stack?

Options :

A : RDMA bypasses the CPU and operating system on the data path, allowing data to be directly transferred between the memory of two systems, reducing CPU overhead and improving data throughput.

B : RDMA allows memory access directly from one host’s CPU to another host-s memory without any involvement of the memory controller, reducing latency.

C : RDMA only works over the physical layer of the InfiniBand stack and requires additional middleware to handle data transfer at the transport layer.

D : RDMA requires the intervention of the OS kernel in both the sending and receiving systems, as it relies on kernel space memory buffers for transferring data between nodes.

Answer: A

Question 2

Which of the following best describes how load balancing is achieved in an InfiniBand fabric using switches?

Options :

A : Load balancing is achieved by dynamically reassigning Local Identifiers (LIDs) to redistribute traffic across available ports.

B : Switches in an InfiniBand fabric use static routes for each connection, making load balancing impossible without manual reconfiguration.

C : InfiniBand switches use Adaptive Routing to dynamically select alternative paths based on real-time congestion information.

D : Load balancing is managed by the Host Channel Adapters (HCAs), and switches play no role in traffic distribution.

Answer: C

Question 3

You are configuring an InfiniBand fabric to optimize high-throughput, low-latency communication for a large-scale HPC cluster. Which advanced InfiniBand feature allows you to ensure traffic prioritization and reduce congestion for critical workloads?

Options :

A : Multipathing (MPIO)

B : Adaptive Routing (AR)

C : Credit-Based Flow Control

D : Service Level (SL) to Virtual Lane (VL) Mapping

Answer: D

Question 4

In AI/ML training clusters, how does Remote Direct Memory Access (RDMA) over InfiniBand accelerate the performance of distributed deep learning?

Options :

A : RDMA over InfiniBand enables deep learning models to share memory across GPUs, allowing for larger models to be trained without memory bottlenecks.

B : RDMA over InfiniBand simplifies the orchestration of distributed training jobs by automating workload balancing across GPUs and nodes.

C : RDMA over InfiniBand automatically compresses the data being transferred, which reduces the bandwidth required for communication between GPUs.

D : RDMA over InfiniBand allows direct data transfer between GPUs without passing through the host CPU, significantly reducing the overhead during parameter updates.

Answer: D

Question 5

You are configuring the HCAs in your InfiniBand environment to maximize performance for a high-throughput, low-latency application. Which tuning parameter should you adjust on the HCA to reduce latency without causing packet loss?

Options :

A : Increase the MTU (Maximum Transmission Unit) to its highest supported value.

B : Decrease the MTU (Maximum Transmission Unit) to minimize the time it takes for small packets to be transmitted.

C : Adjust the Receive Queue Depth (RQD) to increase the ability to handle incoming traffic bursts.

D : Tune the SL (Service Level) to prioritize the application traffic over other traffic.

Answer: C

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