NS0-901 Questions Exam, New NS0-901 Test Syllabus

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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q13-Q18):

NEW QUESTION # 13
A data scientist on the team wants to run an experiment on a new vector indexing strategy. To do this, they need a temporary, writable copy of the main vector database. They use the NetApp DataOps Toolkit for Python to automate this process.
The following script is executed:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="prod-vector-db-pvc",
new_pvc_name="exp-indexing-clone",
namespace="ds-prod"
)
Assuming the source PVC exists and the user has correct permissions, what is the primary benefit of using this method?

A. It creates a full physical copy of the data, providing the best performance isolation.
B. It uses NetApp FlexClone technology to create a near-instantaneous, space-efficient writable copy of the volume, accelerating the experimental workflow.
C. It automatically migrates the data from the AFF A-Series to the StorageGRID system for the experiment.
D. It creates a read-only Snapshot, ensuring the integrity of the experimental data.

Answer: B

NEW QUESTION # 14
An organization is developing a new AI-powered application. The initial phase involves feeding a curated 50 TB dataset of labeled images into a complex neural network, allowing the model to learn and adjust its internal parameters over millions of iterations. The second phase involves deploying this finalized model to a web service where it will process single, user-uploaded images and return a classification in real-time.
Which statement accurately describes these two phases?

A. Both Phase 1 and Phase 2 are examples of training.
B. Phase 1 is training, and Phase 2 is inferencing.
C. Both Phase 1 and Phase 2 are examples of inferencing.
D. Phase 1 is inferencing, and Phase 2 is training.

Answer: B

NEW QUESTION # 15
Which of the following describes the impact of generative AI in content creation?

A. Generative AI predicts customer preferences without creating new content.
B. Generative AI only processes pre-existing content.
C. Generative AI can create new content such as text, images, and videos.
D. Generative AI analyzes data without generating content.

Answer: C

NEW QUESTION # 16
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 predictive AI model to classify legal documents.
C. A fine-tuning pipeline to update the LLM daily.
D. A new LLM trained from scratch on the legal documents.

Answer: A

NEW QUESTION # 17
Which of the following best describes the difference between data lakes, data warehouses, and lakehouses?

A. Data lakes store metadata, data warehouses store transaction data, and lakehouses store archival data.
B. Data lakes store structured data, data warehouses store unstructured data, and lakehouses store only real-time data.
C. Data lakes store raw, unstructured data, data warehouses store structured data, and lakehouses combine the features of both.
D. Data lakes store data in cloud storage, data warehouses store it in traditional databases, and lakehouses store it in external drives.

Answer: C

NEW QUESTION # 18
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