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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Lifecycle	27%	- AI lifecycle stages: design, training, deployment, monitoring - Data preparation and management for AI - Predictive vs generative AI - Model training, inference, and optimization - AI governance, ethics, and compliance
Topic 2: Cloud and Hybrid Cloud AI Deployment	18%	- Hybrid and multi-cloud AI architectures - Data mobility and consistency across environments - Cloud-native AI solutions and integration - NetApp cloud data services for AI
Topic 3: AI Overview	15%	- 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 - Convergence of AI, high-performance computing, and analytics
Topic 4: NetApp AI Solutions and Architecture	25%	- ONTAP integration with AI frameworks - Data management and data pipeline design - Storage architectures for AI workloads - Scalability and performance optimization for AI - NetApp AI-ready infrastructure components
Topic 5: Security, Reliability, and Operations	15%	- Data security and access control for AI - Monitoring, logging, and troubleshooting AI environments - High availability and data protection - Cost management and efficiency

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q14-Q19):

NEW QUESTION # 14
What is the primary difference between C-Series and A-Series storage for AI workloads?

A. A-Series provides GPU-based acceleration, while C-Series only supports CPU-based workloads.
B. A-Series is for smaller-scale AI workloads, while C-Series supports large-scale, high-performance AI applications.
C. C-Series is designed for high-performance workloads, while A-Series focuses on cost-efficiency for general storage.
D. C-Series supports both file and object-based storage, while A-Series is optimized for object storage.

Answer: C

NEW QUESTION # 15
The company decides to establish a disaster recovery (DR) site in a secondary data center for the entire Digital Twin platform. The DR plan must protect the HPC data, the AI training data, and the central data lake.
The DR requirements are:
- RPO: 4 hours for all data.
- RTO: 24 hours for the entire platform.
- Process: The failover and failback process should be as automated as possible.
Which combination of technologies provides the most comprehensive DR solution?

A. Use BlueXP backup and recovery to back up all three storage systems (ASA, E-Series, StorageGRID) to a single cloud target.
B. Use NetApp SnapMirror for the ASA, StorageGRID's built-in replication for the data lake, and a host- based tool like 'rsync' for the E-Series/BeeGFS data, all orchestrated via BlueXP disaster recovery.
C. Deploy a MetroCluster configuration spanning the primary and DR sites for all three storage systems.
D. Use NetApp SnapMirror for the ASA, StorageGRID replication for the data lake, and rely on tape backups for the E-Series/BeeGFS data.

Answer: B

NEW QUESTION # 16
An architect is designing a fully automated, end-to-end MLOps pipeline on Kubernetes for a computer vision use case. The pipeline must handle everything from data versioning to model deployment.
The required pipeline stages are:
1. Data Versioning: Create a new, immutable version of the master dataset for the pipeline run.
2. Data Preparation: Launch a pod to run a preprocessing script on the versioned data.
3. Model Training: Launch a distributed training job that reads the prepared data from a highperformance volume.
4. Model Deployment: Push the trained model to a production inference service.
Which combination of NetApp and Kubernetes technologies provides the most effective and automated solution for this entire pipeline?

A. Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
B. Use the NetApp DataOps Toolkit to create a Snapshot of the source data volume (for versioning), then create a FlexClone PVC from the snapshot for the preparation stage, and finally create a FlexGroup PVC for the training stage.
C. Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.
D. Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.
E. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.

Answer: B

NEW QUESTION # 17
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.)

A. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
B. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
C. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
D. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
E. Use a standard 10GbE network for all connectivity to reduce costs.
F. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.

Answer: A,C,D,F

NEW QUESTION # 18
An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?

A. A Retrieval-Augmented Generation (RAG) architecture.
B. A new LLM trained from scratch on the legal documents.
C. A fine-tuning pipeline to update the LLM daily.
D. A predictive AI model to classify legal documents.

Answer: A

NEW QUESTION # 19
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