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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
AI Lifecycle and Deployment	- Operational challenges 1. Scalability and performance optimization 2. Data governance and management - End-to-end AI lifecycle 1. Model training and validation 2. Data ingestion and preparation 3. Model deployment and monitoring
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	- Algorithm types 1. Supervised learning 2. Unsupervised learning 3. Reinforcement learning - AI, ML, DL concepts 1. Training, inference, and prediction workflows 2. Differences between AI, machine learning, and deep learning
Industry Use Cases	- AI applications across industries 1. Digital twins 2. Autonomous systems and agents 3. Healthcare AI

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q82-Q87):

NEW QUESTION # 82
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 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.
B. A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.
C. A data lake is only for on-premises deployments, while a data warehouse is only for cloud deployments.
D. A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.

Answer: A

NEW QUESTION # 83
A national research laboratory is investing in a turnkey AI infrastructure solution. Their primary goal is to eliminate the complexity and risk of designing and integrating the compute, network, and storage components themselves. The solution must be pre-validated by the vendors to deliver predictable, linear performance as they scale from one to multiple compute nodes. Which two options represent this type of pre-validated, converged infrastructure solution for AI? (Choose two)

A. A single NetApp FAS system connected directly to a single server.
B. A public cloud provider's general-purpose virtual machine instances.
C. NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking.
D. A custom-built server rack with components sourced from various different vendors.
E. A Cisco and NetApp FlexPod for AI solution.

Answer: C,E

NEW QUESTION # 84
An architect is designing a data pipeline for a predictive AI model that will forecast retail sales.
The pipeline must be robust, version-controlled, and efficient.
The proposed data flow is as follows:
1. Ingest: Raw sales data is copied daily from multiple point-of-sale (POS) systems to a central staging area on an on-premises ONTAP cluster.
2. Prepare: The raw data is messy. A data engineering team needs a clean, isolated, and writable copy of the latest daily data to perform cleansing and feature engineering tasks without impacting the original raw data.
3. Train: Once prepared, the cleansed dataset is used to retrain the predictive model on a GPU cluster.
This step must be repeatable with the exact same dataset for compliance.
4. Deploy: The newly trained model is pushed to production inference servers.
Which combination of NetApp technologies best supports this entire predictive AI lifecycle?
(Select all
that apply.)

A. Use a RAG architecture for the sales forecasting model.
B. Use NetApp FlexClone to create an instantaneous, space-efficient, writable copy of the daily raw data for the data preparation stage.
C. Use BlueXP backup and recovery to perform the initial data ingest from the POS systems.
D. Use NetApp XCP to efficiently aggregate the raw sales data from POS systems into the central staging area.
E. Use NetApp StorageGRID as the primary storage for the high-performance training stage.
F. Use NetApp Snapshots on the prepared dataset volume just before training to create an immutable, point-in-time version for compliance and reproducibility.

Answer: B,D,F

NEW QUESTION # 85
What is the primary architectural advantage of using a NetApp AIPod with NVIDIA DGX servers for the AI training cluster, as described in the scenario?

A. It is a reference architecture that is pre-validated by NetApp and NVIDIA to eliminate design complexity and ensure predictable performance for AI workloads.
B. It prioritizes CPU performance over GPU performance for traditional machine learning algorithms.
C. It is designed for small-scale, departmental AI projects and cannot be scaled.
D. It exclusively uses object storage, which simplifies access for data scientists using S3-native tools.

Answer: A

NEW QUESTION # 86
An architect is designing a global infrastructure for a company that develops AI for autonomous vehicles.
The design must accommodate three distinct locations and functions:
1. Edge (Test Tracks): Fleets of test cars generate 100s of TBs of sensor data per day. This data must be ingested locally with high performance.
2. Core (Primary Data Center): The raw data from all edge sites must be aggregated here. This location houses the primary data lake and the main GPU cluster for large-scale model training.
3. Cloud (Public Cloud Provider): Data scientists want to use cloud-native tools for experimental data processing and model development. They also need a cost-effective location for long-term archiving of raw data.
Which combination of deployment locations and NetApp technologies creates the most logical and efficient end-to-end solution?

A. Use NetApp E-Series at the edge, NetApp ASA at the core, and NetApp StorageGRID in the cloud.
Use SnapMirror to move data between all three tiers.
B. Use Cloud Volumes ONTAP at the edge, NetApp StorageGRID at the core, and on-premises ONTAP for cloud archive.
C. Deploy a single, global NetApp StorageGRID across all three locations to act as a unified data plane.
D. Use NetApp ONTAP systems at the edge and core, and Cloud Volumes ONTAP in the public cloud.
Use SnapMirror to replicate data from edge to core, and FabricPool to tier data from the core to the cloud.

Answer: D

NEW QUESTION # 87
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