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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Lifecycle | 27% | - Data preparation and management for AI - AI governance, ethics, and compliance - Model training, inference, and optimization - Predictive vs generative AI - AI lifecycle stages: design, training, deployment, monitoring |
| Topic 2: NetApp AI Solutions and Architecture | 25% | - Data management and data pipeline design - ONTAP integration with AI frameworks - Scalability and performance optimization for AI - NetApp AI-ready infrastructure components - Storage architectures for AI workloads |
| Topic 3: Cloud and Hybrid Cloud AI Deployment | 18% | - Cloud-native AI solutions and integration - Hybrid and multi-cloud AI architectures - Data mobility and consistency across environments - NetApp cloud data services for AI |
| Topic 4: 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 5: Security, Reliability, and Operations | 15% | - Cost management and efficiency - Monitoring, logging, and troubleshooting AI environments - High availability and data protection - Data security and access control for AI |
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NEW QUESTION # 54
A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?
Answer: C
NEW QUESTION # 55
In the data pipeline, which NetApp technology is used to create an efficient, block-level, incremental replication of the anonymized datasets from the on-premises ASA to the Cloud Volumes ONTAP instance in Azure?
Answer: C
NEW QUESTION # 56
An AI infrastructure team is selecting a storage protocol for a new, high-performance computing (HPC) cluster that will be used for a weather modeling AI application. The application requires extremely high-throughput, parallel access from hundreds of compute nodes to a shared dataset.
The workload characteristics are as follows:
Access_Pattern: Massively parallel reads from many clients.
File_Type: Large, shared data files.
Latency_Sensitivity: High (for metadata operations).
Primary_Requirement: Maximum aggregate throughput.
Which storage protocol is best suited for this workload?
Answer: D
NEW QUESTION # 57
An automotive company runs crash simulations on a dedicated High-Performance Computing (HPC) cluster and trains computer vision models on a separate AI cluster. Data scientists are complaining about the long delays required to move terabytes of simulation output data from the HPC storage to the AI cluster's storage before they can begin training.
The current data flow is as follows:
HPC Cluster -> --Manual Copy (NFS)--> -> AI Cluster
An architect has been asked to redesign the infrastructure to eliminate this data movement bottleneck.
Which architectural change would be most effective?
Answer: D
NEW QUESTION # 58
The AI training jobs on the AIPod are performing below expectations. The NVIDIA DGX servers' GPUs show low utilization. A performance analysis reveals that the bottleneck is not the storage system itself, but the network path between the storage and the compute nodes.
The current network configuration is as follows:
Network_Fabric: 100GbE Standard Ethernet
Protocol: NFS over TCP/IP
Data_Path: Storage -> Host CPU -> GPU Memory
Which network architecture enhancement would provide the most significant performance improvement by reducing latency and CPU overhead?
Answer: D
NEW QUESTION # 59
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