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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Management | 28% | - Software and container lifecycle management - Resource allocation and scheduling - GPU cluster and compute node management - Access control and security policies |
| Topic 2: Installation and Deployment | 32% | - Driver and firmware installation - NVIDIA software stack deployment - Container orchestration and resource management - AI cluster setup and configuration |
| Topic 3: Workload Management | 20% | - Data pipeline and storage integration - Framework and runtime configuration - AI workload deployment and scaling - Job scheduling and queue management |
| Topic 4: Troubleshooting and Optimization | 20% | - Throughput and latency optimization - System health and reliability maintenance - Performance monitoring and analysis - Fault diagnosis and resolution |
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NEW QUESTION # 16
A system administrator needs to optimize the delivery of their AI applications to the edge.
What NVIDIA platform should be used?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIAFleet Commandis the platform designed specifically to optimize and manage the deployment and delivery of AI applications at the edge. It enables secure and scalable orchestration of AI workloads across distributed edge devices, providing lifecycle management, remote monitoring, and updates. Fleet Command facilitates running AI applications closer to where data is generated (edge), improving latency and operational efficiency.
* Base Command Platform and Base Command Manager primarily target data center and AI cluster management for configuration, monitoring, and troubleshooting.
* NetQ is focused on network telemetry and network state monitoring rather than application delivery.
Therefore, for AI application delivery and optimization at the edge,Fleet Commandis the recommended NVIDIA platform.
NEW QUESTION # 17
You observe high CPU utilization during data loading in your BCM pipeline. Which of the following techniques can mitigate this bottleneck?
Answer: E
Explanation:
A faster storage system improves data access. Data prefetching hides latency. Compression reduces transfer size. Parallel loading distributes the workload. All these options help reduce the bottleneck.
NEW QUESTION # 18
You need to deploy a containerized AI application from NGC using a CI/CD pipeline. The pipeline should automatically build, test, and deploy the container image to a Kubernetes cluster whenever changes are pushed to the code repository. Which of the following CI/CD tools and practices are most suitable for this scenario?
Answer: B,D,E
Explanation:
B, D and E are the correct. GitLab CI/CD with kaniko and Helm provides a robust and scalable solution for building and deploying container images. NGC CLI allows fetching and deploying pre-built containers, simplifying the process. AWS CodePipeline, CodeBuild, and EKS offer a complete CI/CD solution within the AWS ecosystem. Option A is a valid but less modern approach. Option C lacks a structured deployment process.
NEW QUESTION # 19
A team is running a large distributed training job across multiple nodes in your Run.ai cluster. They are experiencing significant performance degradation due to network latency between the nodes. What are the possible solutions you can implement with Run.ai and potentially ACM to mitigate this issue?
Answer: A,E
Explanation:
RDMA is a key technology for reducing network latency in distributed training. It allows direct memory access between GPUs on different nodes, bypassing the CPU and reducing overhead. Configuring node affinity to keep the nodes within the same rack or network segment minimizes physical distance and network hops, further reducing latency. Increasing GPUs per node can help but is not directly addressing the inter- node latency issue. Data vs. model parallelism is an application-level choice. Run.ai doesn't have built-in network acceleration as a specific feature, but it supports the underlying technologies like RDMA.
NEW QUESTION # 20
You are developing a DOCA application that needs to handle network packets at line rate. Which of the following DOCA services would be most suitable for achieving this goal and why?
Answer: A,E
Explanation:
DOCA Flow is designed for high-performance packet processing and allows offloading flow rules to the DPU hardware. DOCA SPP also plays a crucial role in line-rate processing with efficient packet buffer management.
NEW QUESTION # 21
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