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| Section | Weight | Objectives |
|---|---|---|
| AI Overview | 15% | - AI, machine learning, and deep learning concepts - AI industry use cases and applications - Convergence of AI, high-performance computing, and analytics - AI deployment models: on-premises, cloud, edge - Algorithm types: supervised, unsupervised, reinforcement learning |
| Cloud and Hybrid Cloud AI Deployment | 18% | - Data mobility and consistency across environments - Hybrid and multi-cloud AI architectures - NetApp cloud data services for AI - Cloud-native AI solutions and integration |
| NetApp AI Solutions and Architecture | 25% | - ONTAP integration with AI frameworks - Data management and data pipeline design - Storage architectures for AI workloads - NetApp AI-ready infrastructure components - Scalability and performance optimization for AI |
| Security, Reliability, and Operations | 15% | - Data security and access control for AI - Monitoring, logging, and troubleshooting AI environments - Cost management and efficiency - High availability and data protection |
| AI Lifecycle | 27% | - Model training, inference, and optimization - AI lifecycle stages: design, training, deployment, monitoring - Data preparation and management for AI - AI governance, ethics, and compliance - Predictive vs generative AI |
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NEW QUESTION # 14
An AI infrastructure engineer is troubleshooting a poorly performing distributed training job. The job is running across multiple nodes, each equipped with powerful GPUs. The engineer observes that overall GPU utilization is unexpectedly low. System-level monitoring on the compute nodes provides the following metrics during a training run.
avg_gpu_utilization: 25%
avg_cpu_iowait_percent: 65%
avg_network_bandwidth_util: 95% (on a 10GbE network)
storage_array_latency: <1ms
Given these metrics, what is the most likely bottleneck causing the low GPU utilization?
Answer: D
NEW QUESTION # 15
A financial services company has deployed a real-time fraud detection model at the edge. The model is designed for low-latency inference. However, monitoring reports indicate that the infrastructure costs are excessively high, and GPU utilization is consistently low. The architect reviews the deployment configuration.
Instance_Type: NVIDIA DGX A100 (8 GPUs)
Storage_Tier: High-Performance All-Flash (NetApp ASA)
Network: 100GbE RoCE
GPU_Utilization_Avg: 5%
Monthly_Cost: $15,000
Workload_Profile: Low-volume, sporadic, real-time predictions
What is the most likely cause of the high costs and low utilization?
Answer: D
NEW QUESTION # 16
A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)
Answer: A,C
NEW QUESTION # 17
An online retail company's recommendation engine, which provides real-time product suggestions to users, is experiencing unacceptable latency. The inference application is running on a correctly-sized edge server, but user requests are taking over 500ms to process. An architect reviews the data access pattern and infrastructure diagram.
Application_Location: Edge Server (In-store)
Data_Source_Location: Core Data Center (On-premises ONTAP)
Data_Required_for_Inference: User profile data, product catalog vectors Network_Path: Edge -> WAN -> Core Data Center Observed_Latency: 550ms What is the most likely cause of the high inference latency?
Answer: B
NEW QUESTION # 18
Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)
Answer: B,C
NEW QUESTION # 19
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