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| Section | Objectives |
|---|---|
| AI Operations Fundamentals | - Introduction to AI systems lifecycle in production - Core concepts of MLOps and LLMOps |
| Security and Governance | - Data privacy and secure model deployment - Compliance and governance in AI systems |
| Infrastructure for AI Workloads | - GPU-accelerated computing environments - Cloud and on-prem AI deployment architectures |
| Model Deployment and Serving | - Model packaging and containerization - Inference serving frameworks and APIs |
| Optimization and Lifecycle Management | - Continuous training and deployment pipelines - Model optimization techniques (quantization, pruning) |
| Monitoring and Observability | - Drift detection and alerting - Performance monitoring for AI models |
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NEW QUESTION # 48
You are an administrator managing a large-scale Kubernetes-based GPU cluster using Run:AI.
To automate repetitive administrative tasks and efficiently manage resources across multiple nodes, which of the following is essential when using the Run:AI Administrator CLI for environments where automation or scripting is required?
Answer: D
Explanation:
When automating tasks with the Run:AI Administrator CLI, it is essential to ensure that the Kubernetes configuration file (kubeconfig) is correctly set up with cluster administrative rights.
This enables the CLI to interact programmatically with the Kubernetes API for managing nodes, resources, and workloads efficiently. Without proper administrative permissions in the kubeconfig, automated operations will fail due to insufficient rights.
NEW QUESTION # 49
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: C
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
NVIDIADOCA(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 asNVMe 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.
* TensorRT is focused on AI inference optimization.
* CUDA is NVIDIA's GPU programming model for general-purpose GPU computing, not for DPUs.
* NSight is a development environment for debugging and profiling NVIDIA GPUs.
Therefore,NVIDIA DOCAis the correct framework for programming BlueField DPUs in a data center environment requiring custom application development and advanced storage/networking integration.
NEW QUESTION # 50
You're encountering intermittent CUDA errors within your Docker container, specifically 'CUDA error: invalid device function'. The application runs fine sometimes, but other times it fails with this error. What are potential causes and debugging strategies?
Answer: A,C,D
Explanation:
A CUDA version mismatch (A) is a common cause of 'invalid device function' errors. GPU overheating (B) can also lead to instability and CUDA errors. Memory access bugs in the CUDA code (D) are another potential cause. While option C might be relevant in some edge cases, it is less likely in a properly configured Docker environment. Insufficient power (E) would typically cause more consistent failures, not intermittent ones.
NEW QUESTION # 51
In a high availability (HA) cluster, you need to ensure that split-brain scenarios are avoided.
What is a common technique used to prevent split-brain in an HA cluster?
Answer: B
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
Aheartbeat networkis a common technique used in HA clusters to continuously monitor the health and availability of cluster nodes. It allows nodes to detect failures and coordinate failover actions, thus preventing split-brain scenarios where multiple nodes believe they are active simultaneously, causing data corruption or conflicts. Manual failover, load balancers, or data replication alone do not prevent split-brain without this monitoring mechanism.
NEW QUESTION # 52
A data scientist submits a Run.ai job requesting 4 GPUs. However, due to resource constraints, only 2 GPUs are immediately available. You want the job to automatically start running as soon as the remaining 2 GPUs become available, without manual intervention. How do you configure Run.ai to achieve this?
Answer: A
Explanation:
Gang scheduling ensures that all requested resources (in this case, all 4 GPUs) are allocated before the job starts. The job will remain in a pending state until all resources are available, and then it will automatically start. 'restartPolicy only applies if a job fails after it has already started. Lower priority would make it less likely to start. Manually suspending and resuming requires intervention. A quota impacts how much you can submit overall, not the allocation of the complete resources requested by a single job.
NEW QUESTION # 53
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