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| Section | Objectives |
|---|---|
| Topic 1: Model Deployment and Serving | - Model packaging and containerization - Inference serving frameworks and APIs |
| Topic 2: Optimization and Lifecycle Management | - Continuous training and deployment pipelines - Model optimization techniques (quantization, pruning) |
| Topic 3: Monitoring and Observability | - Drift detection and alerting - Performance monitoring for AI models |
| Topic 4: AI Operations Fundamentals | - Core concepts of MLOps and LLMOps - Introduction to AI systems lifecycle in production |
| Topic 5: Security and Governance | - Data privacy and secure model deployment - Compliance and governance in AI systems |
| Topic 6: Infrastructure for AI Workloads | - Cloud and on-prem AI deployment architectures - GPU-accelerated computing environments |
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NEW QUESTION # 28
You are troubleshooting an issue where a container inside a pod is unable to access the NVIDIA GPU. The NVIDIA Device Plugin is running, and the pod is requesting 'nvidia.com/gpu: 1'. What are the potential causes for this issue?
Answer: A,B,C,E
Explanation:
The correct answers are A, B, C, and D. Several factors can prevent a container from accessing the GPU. Incorrectly installed NVIDIA drivers (A) mean the device plugin cannot function. A misconfigured NVIDIA Container Toolkit (B) prevents the correct GPU passthrough. Missing NVIDIA libraries in the container image (C) lead to runtime errors. SELinux policies (D) can block device access. While E is possible, it usually leads to scheduling failures rather than the pod running without GPU access. The scheduler should prevent over-subscription.
NEW QUESTION # 29
What is the primary advantage of using a disaggregated infrastructure for AI workloads compared to a traditional converged infrastructure?
Answer: A
Explanation:
Disaggregated infrastructure allows you to scale compute, storage, and networking independently based on the specific needs of your AI workloads. Converged infrastructure typically scales in predefined units, which can lead to resource wastage. While disaggregation can lead to other benefits, independent scaling is the primary advantage for AI workloads with varying resource demands.
NEW QUESTION # 30
You're optimizing a BCM pipeline that processes images. You notice that the CPU is consistently at 100% utilization, while the GPU is underutilized. Which optimization strategy is MOST likely to improve performance?
Answer: A
Explanation:
Offloading CPU-intensive tasks to the underutilized GPU and implementing asynchronous data transfer are both effective strategies to reduce CPU bottleneck and utilize GPU resources more efficiently.
NEW QUESTION # 31
An administrator is troubleshooting a bottleneck in a deep learning run time and needs consistent data feed rates to GPUs.
Which storage metric should be used?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
When troubleshooting performance bottlenecks related to feeding data consistently to GPUs during deep learning workloads, the key storage metric to consider is sequential read speed. Deep learning training typically involves streaming large datasets sequentially from storage to GPUs. The sequential read speed measures how fast data can be read in a continuous stream, directly impacting the ability to keep GPUs fed without stalls.
* Disk I/O operations per second (IOPS) measures random read/write operations and is less relevant for large sequential data streams in AI workloads.
* Disk free space indicates available storage capacity but does not impact data feed rate.
* Disk utilization in performance manager shows overall usage but does not specify the speed or consistency of data feed.
Therefore, focusing on sequential read speed (option C) is critical for ensuring consistent, high- throughput data feeding to GPUs, minimizing bottlenecks in deep learning runtime environments.
This is consistent with NVIDIA AI Operations best practices for system performance optimization and troubleshooting storage-related issues in AI infrastructure.
NEW QUESTION # 32
How can you ensure that all newly provisioned nodes in your BCM cluster automatically have the necessary NVIDIA drivers and container runtime installed?
Answer: A,D
Explanation:
A custom OS image ensures drivers and runtime are present from the start. A post-provisioning script allows automated installation. Manual installation is not scalable. A DaemonSet installs software after the node joins the cluster, but BCM configuration happens at provisioning. The NVIDIA automatic driver installation tool might not be compatible with all BCM configurations.
NEW QUESTION # 33
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