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| Section | Weight | Objectives |
|---|---|---|
| Security, Reliability, and Operations | 15% | - Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI - High availability and data protection - Cost management and efficiency |
| AI Overview | 15% | - Convergence of AI, high-performance computing, and analytics - Algorithm types: supervised, unsupervised, reinforcement learning - AI deployment models: on-premises, cloud, edge - AI, machine learning, and deep learning concepts - AI industry use cases and applications |
| Cloud and Hybrid Cloud AI Deployment | 18% | - Data mobility and consistency across environments - Hybrid and multi-cloud AI architectures - Cloud-native AI solutions and integration - NetApp cloud data services for AI |
| AI Lifecycle | 27% | - AI governance, ethics, and compliance - Data preparation and management for AI - Model training, inference, and optimization - AI lifecycle stages: design, training, deployment, monitoring - Predictive vs generative AI |
| NetApp AI Solutions and Architecture | 25% | - Scalability and performance optimization for AI - NetApp AI-ready infrastructure components - Data management and data pipeline design - Storage architectures for AI workloads - ONTAP integration with AI frameworks |
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The NetApp Certified AI Expert Exam (NS0-901) certification helps you advance your career and even secure a pay raise. Today, the Network Appliance certification is an excellent choice for career growth, and to obtain it, you need to pass the NS0-901 exam which is a time-based exam. To prepare for the NS0-901 Exam successfully in a short time, it's essential to prepare with real NS0-901 exam questions. If you don't prepare with NS0-901 updated dumps, you will fail and lose time and money.
NEW QUESTION # 22
A data science team works primarily at a central data center but needs to run a short-term, burst- compute training job in the public cloud to take advantage of specialized GPUs that are not available on- premises. They need to efficiently and securely move a 20 TB dataset from their on- premises ONTAP cluster to a Cloud Volumes ONTAP instance for the duration of the job.
The data flow requirement is as follows:
Source: On-premises ONTAP cluster
Destination: Cloud Volumes ONTAP in AWS
Requirement: Efficient, secure, block-level data transfer.
Which NetApp technology is the most appropriate tool for this task?
Answer: B
NEW QUESTION # 23
Which of the following platforms provides tools for model training and deployment specifically for AI workloads?
Answer: A
NEW QUESTION # 24
The "Advisor Assistant" application, running as a pod in Kubernetes, suddenly cannot access its data on the AFF A-Series. The application logs show "connection timed out" errors. The network team provides a firewall log snippet for the traffic between the application pod and the storage system's NFS data LIF.
TIME | SRC_IP | DST_IP | PROTO | DST_PORT | ACTION
-|--||-|-|-
2025-07-11T16:01:10Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 111 | BLOCKED 2025-07-
11T16:01:12Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 2049 | BLOCKED What is the most likely cause of the connectivity failure?
Answer: B
NEW QUESTION # 25
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: A
NEW QUESTION # 26
A university is building a shared AI research platform. They have two primary requirements:
1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
The data in this area is around 50 TB.
2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)
Answer: A,D,E,F
NEW QUESTION # 27
......
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