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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Troubleshooting and Optimization | 20% | - Throughput and latency optimization - Fault diagnosis and resolution - Performance monitoring and analysis - System health and reliability maintenance |
| Topic 2: Workload Management | 20% | - Data pipeline and storage integration - Job scheduling and queue management - Framework and runtime configuration - AI workload deployment and scaling |
| Topic 3: Management | 28% | - GPU cluster and compute node management - Resource allocation and scheduling - Software and container lifecycle management - Access control and security policies |
| Topic 4: Installation and Deployment | 32% | - AI cluster setup and configuration - Container orchestration and resource management - NVIDIA software stack deployment - Driver and firmware installation |
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NEW QUESTION # 86
You are managing a high availability (HA) cluster that hosts mission-critical applications. One of the nodes in the cluster has failed, but the application remains available to users.
What mechanism is responsible for ensuring that the workload continues to run without interruption?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In an HA cluster, thefailover mechanismis responsible for detecting node failures and automatically transferring workloads to a standby or redundant node to maintain service availability. This process ensures mission-critical applications continue running without interruption. Load balancing helps distribute traffic but does not handle node failures. Manual intervention is not ideal for HA, and data replication ensures data integrity but does not itself manage workload continuity.
NEW QUESTION # 87
You are responsible for deploying a deep learning model for real-time inference using Triton Inference Server from NGC. Latency is a critical requirement. Which of the following optimization techniques can you employ to minimize inference latency?
Answer: A,B,C,D
Explanation:
A, B, C and E are correct. Increasing instances allows for parallel processing. Dynamic batching improves throughput. Reducing precision accelerates computation. Model optimization enhances GPU utilization. D is generally incorrect; CUDA graphs typically improve performance by reducing kernel launch overhead.
NEW QUESTION # 88
A data scientist has provided you with a Jupyter Notebook running inside an NGC container. This notebook relies on a large dataset stored in an object storage service (e.g., AWS S3, Google Cloud Storage). What's the most efficient and secure way to provide the notebook access to this data without embedding credentials directly into the notebook or container image?
Answer: D,E
Explanation:
C and E are the most secure and efficient. Kubernetes Secrets allow for secure storage and management of sensitive data, which can be mounted into the container as files. Leveraging IAM roles or Service Accounts allows the container to inherit permissions from the Kubernetes cluster, eliminating the need for explicit credentials. Option B is less secure as environment variables can be easily exposed. Option A can introduce performance bottlenecks. Option D is highly discouraged due to security risks and lack of flexibility.
NEW QUESTION # 89
What two (2) platforms should be used with Fabric Manager? (Choose two.)
Answer: A,D
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIA Fabric Manager is designed to manage and optimize fabric resources like NVLink and NVSwitch in enterprise-class platforms such as HGX and DGX systems. These platforms have the necessary hardware fabric components. The L40S Certified and GeForce series are either not compatible or do not require Fabric Manager.
NEW QUESTION # 90
You are setting up a data center for AI research that requires both high-performance computing (HPC) for model training and interactive data science workstations. How would you optimally partition your GPU resources using NVIDIA vGPU?
Answer: E
Explanation:
Profiling and dynamic adjustment of vGPU profiles are crucial for optimal resource allocation. Different workloads have different resource needs. HPC benefits from large slices, while interactive workstations can function well with smaller slices. A fixed profile will likely lead to underutilization or performance bottlenecks. Oversubscribing without careful monitoring can lead to severe performance degradation. Limiting data scientists to CPU-based processing wastes valuable GPU resources.
NEW QUESTION # 91
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