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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Infrastructure and NetApp Solutions	- AI-ready data infrastructure 1. High-performance storage for AI workloads 2. Data pipeline optimization for AI/ML workloads - Converged workloads 1. Shared infrastructure design considerations 2. AI, HPC, and analytics convergence
Topic 2: Industry Use Cases	- AI applications across industries 1. Digital twins 2. Autonomous systems and agents 3. Healthcare AI
Topic 3: AI and Machine Learning Fundamentals	- Algorithm types 1. Reinforcement learning 2. Supervised learning 3. Unsupervised learning - AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows
Topic 4: AI Lifecycle and Deployment	- Operational challenges 1. Data governance and management 2. Scalability and performance optimization - 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 (Q22-Q27):

NEW QUESTION # 22
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 fine-tuning pipeline to update the LLM daily.
C. A predictive AI model to classify legal documents.
D. A new LLM trained from scratch on the legal documents.

Answer: A

NEW QUESTION # 23
Which of the following platforms provides tools for model training and deployment specifically for AI workloads?

A. Google VertexAI
B. All of the above
C. Domino Data Labs
D. RunAI

Answer: B

NEW QUESTION # 24
A research lab uses a fleet of autonomous drones to collect high-resolution aerial imagery for agricultural analysis. The drones land at a remote edge location and offload their data. The AI models for image analysis are trained at a central data center. The team is using NetApp SnapMirror to replicate the data from the edge to the core. However, the data scientists are complaining that the datasets arriving at the data center are often incomplete or corrupted.
An administrator reviews the SnapMirror configuration and status via the BlueXP API:
{
"source": { "workingEnvironmentId": "OnPrem-Edge-Filer-1", "volumeName": "drone_data_raw" },
"destination": { "workingEnvironmentId": "Core-Datacenter-A800", "volumeName":
"drone_data_replicated" },
"mirrorState": "broken-off",
"relationshipStatus": "idle",
"unhealthyReason": "Transfer failed. Destination volume is out of space.", "lastTransferInfo": {
"transferError": "No space left on device"
}
}
What is the direct cause of the incomplete datasets at the data center?

A. The BlueXP Connector does not have the correct permissions to manage the SnapMirror relationship.
B. The network connection between the edge and the core is unreliable.
C. The source volume at the edge location has become corrupted.
D. The SnapMirror relationship is broken because the destination volume at the core data center has run out of capacity.

Answer: D

NEW QUESTION # 25
A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?

A. The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
B. Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
C. Both the data scientist and the DevOps engineer should use automated production pipelines.
D. The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).

Answer: D

NEW QUESTION # 26
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. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
B. Use a standard 10GbE network for all connectivity to reduce costs.
C. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
D. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
E. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
F. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.

Answer: A,C,E,F

NEW QUESTION # 27
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