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NEW QUESTION 43
A user reports that they are unable to submit jobs to a specific partition. You’ve verified that the partition exists and is enabled. What are the possible reasons for this?

The user’s account is not associated with the partition.

The partition’s state is set to INACTIVE.

The ‘MaxNodes’ parameter for the partition is set to 0.

The user has exceeded their QOS limit.

All of the above

NEW QUESTION 44
In a high availability (HA) cluster, you need to ensure that split-brain scenarios are avoided.
What is a common technique used to prevent split-brain in an HA cluster?

Configuring manual failover procedures for each node.

Using multiple load balancers to distribute traffic evenly across nodes.

Implementing a heartbeat network between cluster nodes to monitor their health.

Replicating data across all nodes in real time.

NEW QUESTION 45
You are using a custom topology with NVSwitches, and ‘nvsm’ is not detecting the correct links. You need to manually define the topology. Which of the following is the correct way to provide a custom topology definition to

Edit the ‘/etc/nvsm/nvsm.topology’ file with the correct connection definitions.

Use the ‘nvsm -topology-file command to specify the topology at runtime.

Manually configure the NVSwitches through their web interface.

Modify the ‘nvswitchd.conf file.

There is no mechanism to manually define the topology for ‘nvsm’ .

NEW QUESTION 46
A system administrator needs to scale a Kubernetes Job to 4 replicas.
What command should be used?

kubectl stretch job –replicas=4

kubectl autoscale deployment job –min=1 –max=10

kubectl scale job –replicas=4

kubectl scale job -r 4

NEW QUESTION 47
A system administrator notices that jobs are failing intermittently on Base Command Manager due to incorrect GPU configurations in Slurm. The administrator needs to ensure that jobs utilize GPUs correctly.
How should they troubleshoot this issue?

Increase the number of GPUs requested in the job script to avoid using unconfigured GPUs.

Check if MIG (Multi-Instance GPU) mode has been enabled incorrectly and reconfigure Slurm accordingly.

Verify that non-MIG GPUs are automatically configured in Slurm when detected, and adjust configurations if needed.

Ensure that GPU resource limits have been correctly defined in Slurm’s configuration file for each job type.

NEW QUESTION 48
When configuring node auto-scaling within BCM for an AI cluster, which of the following metrics would be the most effective indicators for triggering scale-up events, ensuring efficient resource utilization for AI workloads?

CPU utilization across the cluster.

Memory utilization across the cluster.

GPU utilization across the cluster.

Network I/O across the cluster.

Disk I/O across the cluster.

NEW QUESTION 49
Your cluster users are complaining about long wait times for interactive jobs. You suspect the default backfill scheduler is not effectively utilizing available resources for these smaller, shorter jobs. What can you do to improve the scheduling of interactive jobs, considering backfill limitations?

Disable the backfill scheduler entirely.

Increase the ‘bf_intervar parameter to check for backfill opportunities more frequently.

Implement a separate partition specifically for interactive jobs with a higher priority and shorter time limit.

Set ‘Scheduler Type=sched/priority’ to prioritize based on job age instead of size.

Decrease the value of

NEW QUESTION 50
You’re using Docker Compose to manage a multi-container application that includes a GPU-accelerated container. The application runs fine locally, but when deployed to a cloud environment, the GPU container fails to start with a ‘device not found’ error. What are the potential reasons for this failure?

The cloud environment does not have NVIDIA GPUs available. Verify that the cloud instance type includes NVIDIA GPUs.

The NVIDIA drivers are not installed on the cloud instance. Install the appropriate NVIDIA drivers for the cloud instance’s operating system.

The Docker daemon on the cloud instance is not configured to use the NVIDIA runtime. Configure the Docker daemon as described in NVIDIA’s documentation.

The Docker Compose file does not specify the ‘-gpus all’ flag for the GPU container. Add ‘deploy: ‘ and ‘resources:’ sections to your docker-compose.yml to specify GPU requirements.

The Docker image is too large to be deployed in the cloud environment. Optimize the Docker image size to reduce deployment time.

NEW QUESTION 51
What is the primary benefit of using NVIDIA MIG in a multi-tenant environment?

Improved CPU performance.

Guaranteed isolation and resource allocation for each tenant.

Increased network bandwidth.

Simplified container deployment.

Decreased memory usage.

NEW QUESTION 52
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?

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

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

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

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

NEW QUESTION 53
An organization only needs basic network monitoring and validation tools.
Which UFM platform should they use?

UFM Enterprise

UFM Telemetry

UFM Cyber-AI

UFM Pro

NEW QUESTION 54
You have a cluster dedicated to AI inference, serving models from a persistent volume. You’re experiencing high latency and CPU usage on the nodes serving inference requests. You suspect that storage access patterns are contributing to the issue. Your persistent volume is backed by a distributed file system. Describe a strategy, including relevant tools and techniques, to analyze the storage I/O profile of your inference workloads and identify potential optimizations.

Use ‘iotop’ or ‘iostat’ on the compute nodes to monitor real-time I/O activity and identify processes with high disk I/O. Then check the related containers that are doing more of these reads/writes.

Capture network traffic using ‘tcpdump’ or Wireshark to analyze the communication patterns between the compute nodes and the storage system. Look for excessive network latency or congestion. Also monitor the network latency using tools like ‘ping’ or ‘iperf.

Utilize the distributed file system’s monitoring tools (if available) to analyze I/O patterns at the file system level. This can reveal hotspots or inefficient data access patterns.

Randomly restart the inference pods. If the issue goes away, it means the storage system was temporarily overloaded.

Implement storage QOS (Quality of Service) policies to prioritize inference workloads and limit the impact of other I/O-intensive processes.

NEW QUESTION 55
Explanation:
You are running a Docker container that utilizes NVIDIA GPUs for deep learning inference. The application inside the container fails to detect the GPUs. You’ve verified that NVIDIA drivers are installed on the host. What is the MOST likely cause and how do you fix it?

The NVIDIA Container Toolkit is not installed or configured correctly on the host machine. Install and configure the toolkit following NVIDIA’s official documentation.

The Docker daemon is not configured to use the NVIDIA runtime. Modify the ‘/etc/docker/daemon.json’ file to include “‘default-runtime”: “nvidia”‘ and restart the Docker daemon.

The CUDA version in the container does not match the driver version on the host. Ensure the container uses a CUDA version compatible with the host driver.

The ‘”gpus all’ flag was not used when running the container. Re-run the container with ‘docker run -gpus all …w.

The host’s firewall is blocking communication between the container and the NVIDIA drivers. Disable the firewall or create an exception for the container network.

NEW QUESTION 56
A BCM pipeline running a large language model (LLM) experiences significant latency during inference. Profiling reveals that the ‘torch.compile’ is taking too much memory and time. What optimization strategies would you consider to improve inference performance?

Utilize quantization techniques (e.g., INT8 or FP16) to reduce the model size and memory footprint.

Employ model parallelism to distribute the LLM across multiple GPUs.

Use techniques like speculative decoding or continuous batching to increase throughput.

Try different ‘mode’ arguments for ‘torch.compile’ such as ‘reduce-overhead’ or ‘max-autotune’.

All of the above.

NEW QUESTION 57
You are using BCM to manage a large cluster of GPU servers. You want to implement a mechanism to automatically scale the number of BCM instances based on the load. What Kubernetes feature would be MOST suitable for this purpose?

Horizontal Pod Autoscaler (HPA)

Vertical Pod Autoscaler (VPA)

Cluster Autoscaler

Node Auto-Provisioning

kube-scheduler

NEW QUESTION 58
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?

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

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

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

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

NEW QUESTION 59
You are setting up a Kubernetes cluster on NVIDIA DGX systems using BCM, and you need to initialize the control-plane nodes.
What is the most important step to take before initializing these nodes?

Set up a load balancer before initializing any control-plane node.

Disable swap on all control-plane nodes before initializing them.

Ensure that Docker is installed and running on all control-plane nodes.

Configure each control-plane node with its own external IP address.

NEW QUESTION 60
You are tasked with deploying a deep learning framework container from NVIDIA NGC on a stand-alone GPU-enabled server.
What must you complete before pulling the container? (Choose two.)

Install Docker and the NVIDIA Container Toolkit on the server.

Set up a Kubernetes cluster to manage the container.

Install TensorFlow or PyTorch manually on the server before pulling the container.

Generate an NGC API key and log in to the NGC container registry using docker login.

NEW QUESTION 61
You are tasked with optimizing a BCM pipeline that processes video streams in real-time. The pipeline frequently misses frames, resulting in dropped video. What are the most effective strategies to reduce frame drops?

Reduce the resolution or frame rate of the input video streams.

Optimize the most computationally intensive stages of the pipeline using GPU acceleration (e.g., TensorRT).

Implement asynchronous processing to allow stages to run in parallel.

Increase the buffer size for video frames to accommodate processing delays.

All of the above.

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