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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q19-Q24):

NEW QUESTION # 19
The firm's CFO is concerned about the rising costs of the on-premises AI infrastructure. A storage utilization report shows that of the 200 TB of data on the high-performance AFF A-Series, 150 TB consists of inactive, older versions of product documents that are rarely accessed but must be kept online for regulatory reasons.
The current storage landscape is:
- Performance Tier: NetApp AFF A-Series (200 TB used)
- Capacity Tier: NetApp StorageGRID (1.5 PB used)
What is the most effective and automated solution to reduce the storage cost of the performance tier without impacting data accessibility?

A. Purchase an additional, larger AFF A-Series system to gain better storage efficiency through deduplication.
B. Implement NetApp FabricPool to automatically tier the inactive data blocks from the AFF A-Series to the StorageGRID system.
C. Use NetApp SnapMirror to replicate the entire 200 TB volume to the StorageGRID system and then delete the source.
D. Manually identify and delete the 150 TB of inactive data from the AFF A-Series.

Answer: B

NEW QUESTION # 20
An architect is designing a scalable, automated MLOps platform using Kubeflow on a Kubernetes cluster. The platform must support the entire AI lifecycle for multiple teams, with different storage requirements at each stage.
The key requirements are:
- Data Ingestion: A pipeline step needs a shared, read-write volume accessible by multiple pods to stage raw data.
- Experimentation: Data scientists need individual, isolated volumes for their Jupyter notebooks.
- Training: Distributed training jobs require a high-performance, parallel-access filesystem for reading training data.
- Automation: All storage must be provisioned automatically via Kubeflow pipeline definitions without manual intervention.
Which combination of technologies and configurations would create the most effective solution?

A. Create a single, large NFS volume and mount it to all pods using a static PersistentVolume.
B. Rely on hostPath volumes for all storage to ensure the highest performance.
C. Use the NetApp DataOps Toolkit for Python within the Kubeflow pipeline components to dynamically create and manage Trident PVCs for each stage.
D. Use the NetApp DataOps Toolkit for all storage provisioning, bypassing Trident and Kubernetes PVCs.
E. Configure multiple Trident backends (e.g., 'ontap-nas' for standard volumes, 'ontap-nas-flexgroup' for parallel access) and corresponding StorageClasses.

Answer: C,E

NEW QUESTION # 21
A media company is building a new generative AI service. The project has two main components:
1. Data Lake & Fine-Tuning: A 300 TB repository of unstructured data (videos, images, text) stored as objects will be used to fine-tune a foundational model. This process requires a scalable, cost-effective storage solution that can integrate with cloud-native data processing tools like Apache Spark.
2. Inference & RAG: The fine-tuned model will be used in a customer-facing application that leverages Retrieval-Augmented Generation (RAG). To ensure low-latency responses, the RAG component requires extremely fast lookups from a 10 TB vector database.
The company needs a solution that optimizes both cost and performance for this entire lifecycle.
Which combination of NetApp technologies provides the most appropriate solution for this scenario?

A. Use a single, large NetApp ASA system for both the object data lake and the vector database.
B. Use NetApp StorageGRID for the data lake and a NetApp ASA system for the vector database.
C. Use NetApp Cloud Volumes ONTAP for the data lake and NetApp StorageGRID for the vector database.
D. Use NetApp E-Series for the data lake and a NetApp ASA system for the vector database.

Answer: B

NEW QUESTION # 22
An AI architect needs to design a complete, end-to-end data pipeline for a new generative AI application at a financial services firm. The application will allow internal analysts to query a massive, 500 TB archive of historical market data and reports to generate summaries. The firm has the following environment and requirements:
Data_Sources: A mix of on-premises ONTAP filers and StorageGRID S3 buckets.
Requirement_1: All queries must be answered using only the private data archive.
Requirement_2: All generated summaries must provide citations to the source reports.
Requirement_3: All data containing client PII must be identified and excluded from the LLM context.
Requirement_4: The solution must be cost-effective for the large, mostly-read data archive.
Which set of actions and technologies constitutes the most robust and compliant solution?
(Select all that apply.)

A. Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
B. Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
C. Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.
D. During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.
E. Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.
F. Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.

Answer: A,D,E,F

NEW QUESTION # 23
A robotics company is developing a control system for an autonomous warehouse drone. The drone must learn to navigate complex environments to pick up packages. The development team has created a physics-based simulation where the drone can attempt the task millions of times.
The drone receives a positive reward for successfully retrieving a package and a negative penalty for collisions. Which type of machine learning algorithm is being used in this scenario?

A. Supervised learning
B. Unsupervised learning
C. Reinforcement learning
D. Generative learning

Answer: C

NEW QUESTION # 24
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