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NVIDIA AI Operations Sample Questions (Q28-Q33):

NEW QUESTION # 28
A Slurm user needs to submit a batch job script for execution tomorrow.
Which command should be used to complete this task?

A. srun -begin=tomorrow
B. submit -begin=tomorrow
C. salloc -begin=tomorrow
D. sbatch -begin=tomorrow

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Slurm cluster administration, the command to submit a batch job script is sbatch. This command schedules the job to be executed by the Slurm workload manager. The option -begin=tomorrow (or --begin=tomorrow) specifies the start time for the job execution, which in this case is set for tomorrow. The other commands have different purposes:
* submit is not a valid Slurm command.
* salloc is used to allocate resources interactively but does not submit batch jobs for scheduled execution.
* srun runs jobs immediately on allocated resources but is typically used to launch tasks in an active job or interactively, not for batch job submission.
Therefore, the correct command to submit a batch job script for future execution is sbatch -begin=tomorrow.

NEW QUESTION # 29
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?

A. Use techniques like speculative decoding or continuous batching to increase throughput.
B. Employ model parallelism to distribute the LLM across multiple GPUs.
C. Utilize quantization techniques (e.g., INT8 or FP16) to reduce the model size and memory footprint.
D. Try different 'mode' arguments for 'torch.compile' such as 'reduce-overhead' or 'max-autotune'.
E. All of the above.

Answer: E

Explanation:
Quantization reduces model size. Model parallelism distributes the load. Speculative decoding and continuous batching increase throughput. And trying different compile modes can yield better performance.

NEW QUESTION # 30
You have a VMI container running on a cloud platform. You need to monitor the GPU utilization (e.g., GPU memory usage, GPU utilization percentage). Which of the following tools is MOST commonly used for this purpose?

A. vmstat
B. ps
C. htop
D. top
E. nvidia-smi

Answer: E

Explanation:
'nvidia-smi' (NVIDIA System Management Interface) is the primary command-line utility for monitoring and managing NVIDIA GPUs. It provides detailed information about GPU utilization, memory usage, temperature, and other metrics.

NEW QUESTION # 31
Consider the following Python code snippet used for reading data from a storage system for AI training:

This code is used in an AI training loop. What storage considerations are most critical to optimize the performance of this code?

A. Using a high-capacity HDD to store the data.
B. Using a storage system with high sequential read throughput.
C. Data compression for reducing storage space
D. Using a storage system with low latency random access.
E. Using a storage system that supports direct GPU memory access (e.g., GPUDirect Storage).

Answer: B,E

Explanation:
NumPy's 'np.load' function typically reads the entire file sequentially into memory. Therefore, high sequential read throughput is crucial. GPUDirect Storage further optimizes performance by enabling direct memory access between the storage and the GPU, bypassing the CPU and system memory bottlenecks. Using a HDD will drastically reduce performance due to its low throughput and high latency.

NEW QUESTION # 32
You're managing a cluster using Kubernetes and Ceph, and your AI training jobs are experiencing storage I/O bottlenecks. You want to use Rook to manage Ceph within Kubernetes effectively. What configurations in Rook and Kubernetes would you verify to optimize storage performance for your AI workloads?

A. Configure Ceph placement groups (PGs) and pools to match the workload characteristics (e.g., number of objects, access patterns).
B. Disable Ceph monitoring within Rook to reduce overhead on the cluster. pool: data').
C. Modify Rook's default storage class to use the 'rbd' provisioner with optimized parameters for AI workloads (e.g., 'imageFeatures: layering'
D. Verify that the Kubernetes pods have appropriate resource requests and limits to prevent resource contention.
E. Ensure that the Ceph OSDs are running on fast storage devices (e.g., NVMe SSDs) and have sufficient resources (CPU, memory).

Answer: A,C,D,E

Explanation:
OSD performance is crucial for Ceph's overall performance. Resource requests/limits prevent pod resource starvation. Optimizing PGs and pools aligns Ceph with the workload. Configuring vrbd' provisioner with optimized parameters will help improve overall performance. Monitoring is important to debug issues, do not disable.

NEW QUESTION # 33
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