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| Section | Objectives |
|---|---|
| Topic 1: Security and Governance | - Data privacy and secure model deployment - Compliance and governance in AI systems |
| Topic 2: AI Operations Fundamentals | - Core concepts of MLOps and LLMOps - Introduction to AI systems lifecycle in production |
| Topic 3: Model Deployment and Serving | - Model packaging and containerization - Inference serving frameworks and APIs |
| Topic 4: Infrastructure for AI Workloads | - GPU-accelerated computing environments - Cloud and on-prem AI deployment architectures |
| Topic 5: Monitoring and Observability | - Drift detection and alerting - Performance monitoring for AI models |
| Topic 6: Optimization and Lifecycle Management | - Continuous training and deployment pipelines - Model optimization techniques (quantization, pruning) |
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NEW QUESTION # 75
Which two (2) ways does the pre-configured GPU Operator in NVIDIA Enterprise Catalog differ from the GPU Operator in the public NGC catalog? (Choose two.)
Answer: C,E
Explanation:
The pre-configured GPU Operator in the NVIDIA Enterprise Catalog differs from the public NGC catalog GPU Operator primarily by its configuration to use a prebuilt vGPU driver image and being configured to use the NVIDIA License System (NLS). These adaptations allow better support for enterprise environments where vGPU functionality and license management are critical.
NEW QUESTION # 76
A system administrator of a high-performance computing (HPC) cluster that uses an InfiniBand fabric for high-speed interconnects between nodes received reports from researchers that they are experiencing unusually slow data transfer rates between two specific compute nodes. The system administrator needs to ensure the path between these two nodes is optimal.
What command should be used?
Answer: C
Explanation:
To verify the optimal communication path and diagnose issues between two nodes in an InfiniBand fabric, the ibtracert command is used. It traces the route that InfiniBand packets take through the fabric, identifying each hop and any potential bottlenecks or faulty links along the path.
NEW QUESTION # 77
You are managing a Slurm cluster with multiple GPU nodes, each equipped with different types of GPUs.
Some jobs are being allocated GPUs that should be reserved for other purposes, such as display rendering.
How would you ensure that only the intended GPUs are allocated to jobs?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
In Slurm GPU resource management, thegres.conffile defines the available GPUs (generic resources) per node, whileslurm.confconfigures the cluster-wide GPU scheduling policies. To prevent jobs from using GPUs reserved for other purposes (e.g., display rendering GPUs), administrators must ensure that only the GPUs intended for compute workloads are listed in these configuration files.
* Properly configuringgres.confallows Slurm to recognize and expose only those GPUs meant for jobs.
* slurm.confmust be aligned to exclude or restrict unconfigured GPUs.
* Manual GPU assignment usingnvidia-smiis not scalable or integrated with Slurm scheduling.
* Reinstalling drivers or increasing GPU requests does not solve resource exclusion.
Thus, the correct approach is to verify and configure GPU listings accurately ingres.confandslurm.confto restrict job allocations to intended GPUs.
NEW QUESTION # 78
You are managing a Slurm cluster with multiple GPU nodes, each equipped with different types of GPUs. Some jobs are being allocated GPUs that should be reserved for other purposes, such as display rendering.
How would you ensure that only the intended GPUs are allocated to jobs?
Answer: A
Explanation:
In Slurm GPU resource management, the gres.conf file defines the available GPUs (generic resources) per node, while slurm.conf configures the cluster-wide GPU scheduling policies. To prevent jobs from using GPUs reserved for other purposes (e.g., display rendering GPUs), administrators must ensure that only the GPUs intended for compute workloads are listed in these configuration files.
NEW QUESTION # 79
You are a Solutions Architect designing a data center infrastructure for a cloud-based AI application that requires high-performance networking, storage, and security. You need to choose a software framework to program the NVIDIA BlueField DPUs that will be used in the infrastructure. The framework must support the development of custom applications and services, as well as enable tailored solutions for specific workloads. Additionally, the framework should allow for the integration of storage services such as NVMe over Fabrics (NVMe-oF) and elastic block storage.
Which framework should you choose?
Answer: A
Explanation:
NVIDIA DOCA (Data Center Infrastructure-on-a-Chip Architecture) is the software framework designed to program NVIDIA BlueField DPUs (Data Processing Units). DOCA provides libraries, APIs, and tools to develop custom applications, enabling users to offload, accelerate, and secure data center infrastructure functions on BlueField DPUs.
DOCA supports integration with key data center services including storage protocols such as NVMe over Fabrics (NVMe-oF), elastic block storage, and network security and telemetry. It enables tailored solutions optimized for specific workloads and high-performance infrastructure demands.
NEW QUESTION # 80
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