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Network Appliance NS0-901 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Security, Reliability, and Operations15%- High availability and data protection
- Monitoring, logging, and troubleshooting AI environments
- Data security and access control for AI
- Cost management and efficiency
Topic 2: Cloud and Hybrid Cloud AI Deployment18%- NetApp cloud data services for AI
- Data mobility and consistency across environments
- Hybrid and multi-cloud AI architectures
- Cloud-native AI solutions and integration
Topic 3: AI Lifecycle27%- Predictive vs generative AI
- Model training, inference, and optimization
- Data preparation and management for AI
- AI lifecycle stages: design, training, deployment, monitoring
- AI governance, ethics, and compliance
Topic 4: NetApp AI Solutions and Architecture25%- Data management and data pipeline design
- Scalability and performance optimization for AI
- ONTAP integration with AI frameworks
- NetApp AI-ready infrastructure components
- Storage architectures for AI workloads
Topic 5: AI Overview15%- AI, machine learning, and deep learning concepts
- AI industry use cases and applications
- Algorithm types: supervised, unsupervised, reinforcement learning
- AI deployment models: on-premises, cloud, edge
- Convergence of AI, high-performance computing, and analytics

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q90-Q95):

NEW QUESTION # 90
Which of the following describes the impact of generative AI in content creation?

Answer: C


NEW QUESTION # 91
An AI team is embarking on a project to train a new, large-scale computer vision model from scratch. The lead architect emphasizes that the success of the project depends on four fundamental inputs that must be available and managed throughout the training process. Which of the following are the four essential requirements for model generation?

Answer: A


NEW QUESTION # 92
The firm has acquired a competitor, and the volume of proprietary documents for the "Advisor Assistant" is expected to triple, exceeding the capacity of the current StorageGRID data lake. The architect needs to expand the data lake's capacity non-disruptively.
The current StorageGRID status is:
System_Health: Nominal
Node_Count: 6 (3 Storage Nodes, 3 Admin/Gateway Nodes)
Usable_Capacity: 1.5 PB
Used_Capacity: 1.4 PB (93%)
What is the standard procedure for scaling the capacity of the on-premises NetApp StorageGRID system?

Answer: A


NEW QUESTION # 93
An AI platform team is investigating poor I/O performance for a specific workload that involves processing hundreds of thousands of small metadata files. The application is running on a Kubernetes cluster with storage provided by a NetApp ONTAP system over NFS. Performance metrics show acceptable network throughput but very high latency for metadata operations (e.g., open, stat, close).
The current storage configuration is as follows:
Storage_System: NetApp AFF A-Series
Protocol: NFSv4.1
Workload_Profile: Metadata-intensive, many small file lookups
Observed_Issue: High latency on metadata operations, slow job completion Which storage architecture would be better suited to handle this specific metadata-intensive workload?

Answer: B


NEW QUESTION # 94
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: D


NEW QUESTION # 95
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