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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Overview | 15% | - AI industry use cases and applications - AI deployment models: on-premises, cloud, edge - AI, machine learning, and deep learning concepts - Algorithm types: supervised, unsupervised, reinforcement learning - Convergence of AI, high-performance computing, and analytics |
| Topic 2: Cloud and Hybrid Cloud AI Deployment | 18% | - NetApp cloud data services for AI - Cloud-native AI solutions and integration - Hybrid and multi-cloud AI architectures - Data mobility and consistency across environments |
| Topic 3: AI Lifecycle | 27% | - AI lifecycle stages: design, training, deployment, monitoring - AI governance, ethics, and compliance - Predictive vs generative AI - Data preparation and management for AI - Model training, inference, and optimization |
| Topic 4: NetApp AI Solutions and Architecture | 25% | - Scalability and performance optimization for AI - NetApp AI-ready infrastructure components - ONTAP integration with AI frameworks - Data management and data pipeline design - Storage architectures for AI workloads |
| Topic 5: Security, Reliability, and Operations | 15% | - Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI - Cost management and efficiency - High availability and data protection |
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NEW QUESTION # 67
An AI research team is experiencing slow model training times. Their performance monitoring indicates that the GPUs are frequently idle, waiting for data. They want to implement a single technology change to create a more direct data path between their storage and GPUs.
Their current setup is as follows:
Compute: Server with NVIDIA A100 GPUs
Storage: NetApp AFF A-Series (All-Flash)
Network: 100GbE Ethernet
Data_Path: Storage -> Host CPU/Memory -> GPU Memory
Which technology should the architect recommend to specifically address this data path inefficiency?
Answer: B
NEW QUESTION # 68
Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)
Answer: B,D
NEW QUESTION # 69
The data scientists report that their Kubernetes-based data preparation jobs are failing. The pods are stuck in a 'Pending' state.
An MLOps engineer runs 'kubectl describe pvc data-prep-pvc-01' and sees the following event:
Type Reason Age From Message
- - - -
Warning ProvisioningFailed 2m15s trident-orchestrator-7b... failed to provision volume with StorageClass "bronze-tier": backend unavailable: no healthy backend with satisfying attributes for storage class "bronze-tier" The engineer checks the Trident backend configurations and finds no backend associated with the "bronze-tier" StorageClass.
What is the root cause of the failure?
Answer: C
NEW QUESTION # 70
The firm's data science team needs to run a high-priority, interactive model analysis job that requires immediate access to two GPUs. However, all GPUs in the cluster are currently allocated to long-running, lower-priority batch training jobs.
The MLOps platform, Run:AI, shows the following queue status:
JOB_ID | PROJECT | STATUS | PRIORITY | GPU_ALLOCATED
||--|-|
batch_job_1 | team_a | Running | Low | 2
batch_job_2 | team_a | Running | Low | 2
batch_job_3 | team_b | Running | Low | 4
interactive_1| team_c | Pending | High | 2 (requested)
How does the Run:AI platform address this resource contention to allow the high-priority job to run?
Answer: C
NEW QUESTION # 71
Which of the following platforms provides tools for model training and deployment specifically for AI workloads?
Answer: C
NEW QUESTION # 72
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