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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Overview	15%	- Algorithm Types 1. Reinforcement learning 2. Unsupervised learning 3. Supervised learning - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - Machine Learning Fundamentals 1. Understand the relationship between AI, machine learning, and deep learning 2. Describe machine learning benefits - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - AI Deployment Models 1. Edge 2. Cloud 3. Benefits and risks of each model 4. On-premises - AI Industry Applications 1. Agents 2. Digital twins 3. Healthcare applications
AI Hardware Architectures	18%	- Infrastructure Topologies 1. Data aggregation and compute topologies - Networking and Storage 1. Network protocols for AI workloads 2. Storage architectures for AI - NetApp Architectures 1. OVX architectures 2. BasePod 3. SuperPOD
AI Software Architectures	18%	- Development Tools 1. NetApp DataOps Toolkit 2. Jupyter notebooks vs. pipelines - Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale
AI Lifecycle	27%	- Generative AI Concepts 1. Fine-tuning 2. Hallucinations 3. Retrieval Augmented Generation (RAG) - Predictive AI vs. Generative AI 1. Large Language Models (LLMs) 2. Distinction between predictive and generative AI 3. Impact of generative content (text, images, video, decision-making) - Data Preparation 1. XCP and CopySync 2. Data aggregation and cleansing 3. NetApp BlueXP Classification - Model Development 1. Fine-tuning workflows 2. Inferencing 3. Model building
AI Common Challenges	22%	- Traceability and Optimization 1. Optimizing data access and movement 2. Ensuring traceability for code, data, and models 3. Maximizing performance in demanding AI workloads - Resource Management 1. Sizing storage and compute resources effectively 2. Controlling costs and securing storage

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q62-Q67):

NEW QUESTION # 62
A development team is building a generative AI application that must answer questions based on a constantly changing internal knowledge base of company documents. They want to provide the model with up-to-date information without altering its core weights and capabilities.
Which approach is most suitable for this requirement?

A. Retrieval-Augmented Generation (RAG), which fetches relevant information from the documents at query time to inform the model's response.
B. Fine-tuning, which retrains the model's last few layers on the new documents.
C. Predictive AI, which analyzes the documents to forecast future questions.
D. Unsupervised learning, which clusters the documents into topics for the model to browse.

Answer: A

NEW QUESTION # 63
An AI infrastructure team is selecting a storage protocol for a new, high-performance computing (HPC) cluster that will be used for a weather modeling AI application. The application requires extremely high-throughput, parallel access from hundreds of compute nodes to a shared dataset.
The workload characteristics are as follows:
Access_Pattern: Massively parallel reads from many clients.
File_Type: Large, shared data files.
Latency_Sensitivity: High (for metadata operations).
Primary_Requirement: Maximum aggregate throughput.
Which storage protocol is best suited for this workload?

A. An object-based storage system using S3.
B. A block-based protocol using iSCSI.
C. A parallel file system like BeeGFS or Lustre.
D. A standard file-based system using NFS.

Answer: C

NEW QUESTION # 64
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. A custom "PII" category within BlueXP classification to identify specific medical record number formats.
B. A custom Python script that uses regular expressions to search file contents.
C. NetApp BlueXP classification, configured to scan the on-premises ASA working environment.
D. NetApp SnapLock to make the source data immutable before scanning.
E. A manual review process where technicians visually inspect each scan for PII.

Answer: A,C

NEW QUESTION # 65
The firm decides to implement a disaster recovery (DR) site for the "Advisor Assistant" application in a secondary data center. The Recovery Point Objective (RPO) is 15 minutes, and the Recovery Time Objective (RTO) is 4 hours. The design must protect both the document data lake and the vector database.
The primary site contains:
- Data Lake: NetApp StorageGRID
- Vector DB: NetApp AFF A-Series
Which combination of technologies and processes provides a complete and robust DR solution?
(Select all that apply.)

A. Use the NetApp DataOps Toolkit to manually script the failover process.
B. Use NetApp SnapMirror to create an asynchronous replication relationship for the AFF A-Series volume containing the vector database, with a schedule of 10 minutes.
C. Use StorageGRID's built-in replication rules to replicate object data from the primary site's grid to a StorageGRID instance at the DR site.
D. Use NetApp FlexCache at the DR site to cache data from the primary site.
E. Use BlueXP disaster recovery to orchestrate and automate the failover and failback workflows for the application and its data dependencies.
F. Rely on tape backups to be shipped to the DR site in the event of a disaster.

Answer: B,C,E

NEW QUESTION # 66
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 is only for on-premises deployments, while a data warehouse is only for cloud deployments.
C. A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.
D. A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.

Answer: A

NEW QUESTION # 67
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