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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Digital twins 3. Autonomous systems and agents
AI Infrastructure and NetApp Solutions	- AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads - Converged workloads 1. Shared infrastructure design considerations 2. AI, HPC, and analytics convergence
AI and Machine Learning Fundamentals	- AI, ML, DL concepts 1. Training, inference, and prediction workflows 2. Differences between AI, machine learning, and deep learning - Algorithm types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning
AI Lifecycle and Deployment	- Operational challenges 1. Scalability and performance optimization 2. Data governance and management - End-to-end AI lifecycle 1. Model deployment and monitoring 2. Data ingestion and preparation 3. Model training and validation

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q102-Q107):

NEW QUESTION # 102
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. Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.
B. Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.
C. Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
D. During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.
E. Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
F. Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.

Answer: B,D,E,F

NEW QUESTION # 103
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. Use the NetApp DataOps Toolkit for all storage provisioning, bypassing Trident and Kubernetes PVCs.
C. Rely on hostPath volumes for all storage to ensure the highest performance.
D. Use the NetApp DataOps Toolkit for Python within the Kubeflow pipeline components to dynamically create and manage Trident PVCs for each stage.
E. Configure multiple Trident backends (e.g., 'ontap-nas' for standard volumes, 'ontap-nas-flexgroup' for parallel access) and corresponding StorageClasses.

Answer: D,E

NEW QUESTION # 104
An architect is explaining different data storage paradigms to a new data engineering team. The team needs to understand where to store different types of data for their AI projects. Which statement accurately describes a key difference between a data lake and a data warehouse?

A. A data lake is only for on-premises deployments, while a data warehouse is only for cloud deployments.
B. A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.
C. A data lake is designed to store vast quantities of raw, multi-format data (structured, semi- structured, and unstructured), while a data warehouse is typically used for storing structured and filtered data that has already been processed for a specific purpose.
D. A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.

Answer: C

NEW QUESTION # 105
To meet HIPAA compliance, the first step in the data pipeline is to identify all medical scans that contain embedded PII. The solution must be automated and capable of scanning data in-place on the on-premises ASA system.
Which two technologies should be used to accomplish this identification and tagging task?
(Choose 2.)

A. NetApp BlueXP classification, configured to scan the on-premises ASA working environment.
B. A manual review process where technicians visually inspect each scan for PII.
C. A custom "PII" category within BlueXP classification to identify specific medical record number formats.
D. A custom Python script that uses regular expressions to search file contents.
E. NetApp SnapLock to make the source data immutable before scanning.

Answer: A,C

NEW QUESTION # 106
An architect is designing the storage and network infrastructure for a new, large-scale AI cluster dedicated to training foundational models. The primary design goal is to achieve the highest possible data throughput and the lowest latency to ensure multi-million dollar GPU resources are never idle. Which two technologies are essential to include in the design to achieve this goal?
(Choose 2.)

A. An InfiniBand or RoCE-capable Ethernet network fabric.
B. NetApp StorageGRID as the primary storage for the training datasets.
C. A 10GbE Ethernet network for all data traffic.
D. A tiered storage architecture using NetApp FabricPool.
E. GPUDirect Storage support on the storage system.

Answer: A,E

NEW QUESTION # 107
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