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| Section | Objectives |
|---|---|
| AI Operations Fundamentals | - Core concepts of MLOps and LLMOps - Introduction to AI systems lifecycle in production |
| Infrastructure for AI Workloads | - Cloud and on-prem AI deployment architectures - GPU-accelerated computing environments |
| Optimization and Lifecycle Management | - Model optimization techniques (quantization, pruning) - Continuous training and deployment pipelines |
| Security and Governance | - Compliance and governance in AI systems - Data privacy and secure model deployment |
| Model Deployment and Serving | - Model packaging and containerization - Inference serving frameworks and APIs |
| Monitoring and Observability | - Performance monitoring for AI models - Drift detection and alerting |
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NEW QUESTION # 29
You have a Kubernetes cluster with several nodes equipped with NVIDIA GPUs. You want to ensure that pods requesting GPUs are only scheduled on nodes that have the appropriate NVIDIA drivers and the NVIDIA Container Toolkit installed. Which Kubernetes feature(s) can you leverage to achieve this?
Answer: A,B
Explanation:
The correct answers are A and E. Taints and Tolerations ensure that pods are not scheduled onto inappropriate nodes. Nodes can be tainted to indicate the lack of NVIDIA drivers or the Container Toolkit, and pods requiring GPUs can tolerate these taints to indicate their compatibility. Node Affinity, in tandem with taints, provides more fine-grained control over scheduling. You can use node affinity to prefer or require that pods with GPU requests are scheduled on nodes labeled with specific NVIDIA hardware or driver versions. Options B, C, and D are not directly relevant to GPU-aware scheduling.
NEW QUESTION # 30
You are using Fleet Command to manage AI model deployments to a diverse fleet of edge devices with varying hardware capabilities.
Some devices are equipped with GPUs, while others rely on CPUs for inference. How can you ensure that the correct version of the AI model is deployed to each device type?
Answer: C
Explanation:
Device targeting with labels is the most efficient and scalable way to manage deployments to diverse hardware. Separate organizations (A) are overly complex. Manual selection (C) is error-prone. Relying on automatic adaptation (D) might not be reliable. Custom scripts (E) add unnecessary complexity when Fleet Command provides built-in features.
NEW QUESTION # 31
Which practice ensures that changes to machine learning models, datasets, and configuration files are tracked, reproducible, and auditable across development, testing, and production environments in an AI operations lifecycle?
Answer: C
Explanation:
Version control systems manage changes in code, datasets, and model artifacts. They provide traceability, reproducibility, and rollback capabilities. This is essential for maintaining consistency and compliance in AI operations across multiple environments and teams.
NEW QUESTION # 32
After completing the installation of a Kubernetes cluster on your NVIDIA DGX systems using BCM, how can you verify that all worker nodes are properly registered and ready?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
The standard method to verify that worker nodes are correctly registered and ready in a Kubernetes cluster is to runkubectl get nodes. This command lists all nodes and their statuses. Nodes showing a status of"Ready" indicates they are properly connected and available to schedule workloads. Checking pods or manual SSH is not the direct or reliable way to verify node readiness.
NEW QUESTION # 33
An AI model training pipeline involves pre-processing large image datasets. The images are initially stored in a cost-effective object storage system. Which approach minimizes latency when transferring data from object storage to the GPUs for training?
Answer: A
Explanation:
Staging data to a high-performance parallel file system before training reduces latency by bringing the data closer to the compute nodes and providing high throughput. Directly accessing object storage introduces network latency, sharing over NFS can bottleneck, and a single SSD or HDD won't provide sufficient IOPS for multiple GPUs.
NEW QUESTION # 34
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