최신 NCP-AIO 무료덤프 - NVIDIA AI Operations

A. Increase the shared memory allocation for the BCM pipeline.

B. Upgrade the GPU driver to the latest version.

C. Reduce the batch size in the BCM pipeline configuration.

D. A, B and C

E. Enable CUDA memory pooling within the BCM framework.

A. Reduce the size of the graph.

B. Switch to a CPU-based implementation.

C. Increase the number of GPUs per node.

D. Use NVSHMEM collectives for bulk data transfers.

E. Disable CUDA-Aware MPI support

A. Implement retry logic with exponential backoff to handle transient API errors.

B. Cache the API responses to reduce the dependency on the remote API.

C. Use a circuit breaker pattern to prevent the pipeline from overwhelming the API during outages.

D. All of the above.

E. Implement error logging and monitoring to track API failures.

A. NVIDIA Data Center GPU Manager (DCGM) for GPU-level metrics, Prometheus for data collection, and Grafana for visualization.

B. Collectd for system metrics, InfluxDB for time-series data storage, and Chronograf for visualization.

C. "nvidia-smi' for GPU metrics, Nagios for alerting, and Graphite for data storage.

D. Ganglia for cluster monitoring, Cacti for network graphing, and MRTG for traffic monitoring.

E. NVIDIA Nsight Systems for performance profiling, ELK stack (Elasticsearch, Logstash, Kibana) for log analysis, and 'top' for system-level monitoring.

A. The NCCL library is not properly initialized.

B. The InfiniBand drivers are outdated.

C. The 'NVSHMEM SYMMETRIC SIZE environment variable is set too low.

D. The CUDA driver version is incompatible with NVSHMEM.

E. 'CUDA_VISIBLE DEVICES is set incorrectly

A. nvidia-smi' alone, without any specific flags, provides per-MIG instance utilization.

B. DCGM (Data Center GPU Manager) provides detailed monitoring metrics for individual MIG instances.

C. Use the Windows Task Manager to view GPU utilization.

D. The 'top' command in Linux provides GPU utilization information.

E. The 'free command in Linux provides GPU memory usage information.

A. DOCA DPI: Utilize DOCA DPI to perform deep packet inspection and identify malicious patterns or signatures within the network traffic. Configure DPI rules based on known threat intelligence feeds and custom signatures.

B. DOCA Flow: Define flow rules to match specific network traffic patterns associated with malicious activity, such as suspicious ports or protocols. Trigger actions such as logging, dropping, or redirecting the traffic for further analysis.

C. DOCA Telemetry: Collect network traffic statistics and flow information using DOCA Telemetry. Analyze the collected data to identify anomalies or suspicious behavior that may indicate malicious activity.

D. DOCA Comm Channel: Communicate the detected intrusions with host server for the purpose of logging.

E. DOCA RegEx: Implement regular expression matching to identify complex patterns within the network traffic, such as malware signatures or exploit attempts. Configure RegEx rules based on known threat patterns.

A. The system's power supply is insufficient. Use power supply with more wattage.

B. The system BIOS does not support MIG. Update the BIOS to the latest version.

C. The MIG configuration was not saved persistently. Use 'nvidia-smi mig -Igip' to save the configuration to the persistence database after creation, then reboot.

D. The NVIDIA driver is outdated. Update the driver to the latest version.

E. MIG instances are automatically deleted after each reboot for security reasons.

A. Disable the backfill scheduler entirely.

B. Decrease the value of

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

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

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

A. Verify that all client nodes have the same BeeGFS client version installed.

B. Investigate if data locality features within BeeGFS are properly configured to ensure that the data accessed by each node is stored close to it.

C. Restart the entire BeeGFS cluster to resolve any temporary inconsistencies.

D. Examine the logs of the BeeGFS client on the affected nodes for errors or warnings.

E. Check the network connectivity between the affected client nodes and the BeeGFS metadata and storage servers (MDS and OSS).

A. The Docker container is not receiving enough CPU resources. Increase the CPU shares or CPU quota allocated to the container.

B. There is CPU contention. Use 'taskset to bind the data loading process to specific CPU cores to reduce CPU switching overhead.

C. The data loading pipeline is a bottleneck. Optimize data loading by using asynchronous data loaders and prefetching data to the GPU.

D. The model is not large enough to saturate the GPU. Consider using a larger model or increasing the complexity of the computations.

E. The batch size is too small. Increase the batch size to improve GPU utilization, but be mindful of GPU memory limitations.

A. Increase the container's memory limit to provide more space for memory allocation.

B. Monitor GPU memory usage with -nvidia-smi' and look for a high degree of fragmentation (small, non-contiguous memory blocks).

C. Use CUDA memory pools to pre-allocate memory and reduce the frequency of memory allocations and deallocations.

D. Use the function in PyTorch (if applicable) to release unused GPU memory.

E. Restart the container regularly to defragment the GPU memory.

A. High 'iowait' means the CPU is waiting for I/O operations to complete. Increase CPU cores.

B. High 'iowait' means the CPU is waiting for I/O operations to complete. Investigate storage performance bottlenecks such as disk saturation, network latency (if using networked storage), or inefficient data access patterns.

C. High 'iowait' means the system is swapping memory to disk. Add more RAM or reduce memory usage.

D. High 'iowait' is normal during large training jobs and does not require any action.

E. High 'iowait' indicates network congestion. Optimize network configuration.

A. Node Affinity

B. Gang Scheduling

C. Fractional GPUs (MIG)

D. Dynamic Resource Allocation using GPU Metrics

E. Guaranteed Quotas

A. Monitor CPU and GPU utilization inside the container using tools like 'top', 'htop' , and 'nvidia-smi' to identify resource bottlenecks.

B. Profile the TensorFlow model using TensorFlow's profiling tools to identify performance bottlenecks in the model's execution.

C. Check the Docker container logs for any error messages or warnings that might indicate a problem.

D. Check network connectivity between the container and any external services it relies on.

E. Restart the Docker container to clear any accumulated memory or resource leaks.

A. No special configuration is needed; Kubernetes automatically detects and uses SR-IOV enabled GPUs.

B. Specify the VF resource in the pod's resource requests (e.g., 'nvidia.com/vf: 1 '

C. Configure the number of VFs to create on each GPU in the node's device tree overlay.

D. Install the NVIDIA SR-IOV device plugin on each node.

E. Enable SR-IOV in the node's BIOS.

A. 4

B. 8

C. 1

D. 16

E. 2

A. Completely removing caching: This is the simplest approach and always provides the best performance.

B. Using the cloud provider's caching service (e.g., AWS CloudFront, Google Cloud CDN): This can improve performance for frequently accessed data but might introduce additional costs and complexity.

C. Using only the object storage's built-in caching mechanisms: This is sufficient for all workloads and eliminates the need for additional caching layers.

D. In-memory caching using tools like Redis or Memcached: This offers very low latency but is limited by the available memory and can be expensive.

E. Local SSD caching on each compute node: This provides fast access but requires managing cache consistency and dealing with limited storage capacity.

A. The AFA is not configured correctly to handle the specific I/O patterns of your AI workload (e.g., random reads, large sequential writes). Check the AFA's configuration settings for block size, caching policies, and prefetching.

B. The AFA is over-provisioned, and the internal garbage collection processes are interfering with I/O operations. Reduce the amount of provisioned space.

C. The AFA's warranty has expired. Renewing the warranty will magically fix the performance issues.

D. The network connection between the compute nodes and the AFA is the bottleneck. IJpgrade the network infrastructure or optimize the data transfer protocols.

E. The AFA's internal controllers are overloaded, even though the overall storage utilization is low. Monitor the controller utilization and consider upgrading the AFA or distributing the workload across multiple AFAs.