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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Common Challenges	22%	- Resource Management 1. Sizing storage and compute resources effectively 2. Controlling costs and securing storage - Traceability and Optimization 1. Maximizing performance in demanding AI workloads 2. Optimizing data access and movement 3. Ensuring traceability for code, data, and models
Topic 2: AI Software Architectures	18%	- Scaling and Orchestration 1. Leveraging BlueXP software tools 2. Scaling AI workloads with Kubernetes - Development Tools 1. NetApp DataOps Toolkit 2. Jupyter notebooks vs. pipelines - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale
Topic 3: AI Overview	15%	- AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Machine Learning Fundamentals 1. Describe machine learning benefits 2. Understand the relationship between AI, machine learning, and deep learning - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - AI Industry Applications 1. Agents 2. Healthcare applications 3. Digital twins - Algorithm Types 1. Reinforcement learning 2. Unsupervised learning 3. Supervised learning - AI Deployment Models 1. On-premises 2. Cloud 3. Edge 4. Benefits and risks of each model
Topic 4: 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
Topic 5: AI Lifecycle	27%	- Model Development 1. Model building 2. Fine-tuning workflows 3. Inferencing - Predictive AI vs. Generative AI 1. Distinction between predictive and generative AI 2. Impact of generative content (text, images, video, decision-making) 3. Large Language Models (LLMs) - Data Preparation 1. NetApp BlueXP Classification 2. XCP and CopySync 3. Data aggregation and cleansing - Generative AI Concepts 1. Retrieval Augmented Generation (RAG) 2. Hallucinations 3. Fine-tuning

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q43-Q48):

NEW QUESTION # 43
A healthcare organization plans to use a large dataset of patient records to train a predictive model. Before training, they must identify and segregate all records containing Personally Identifiable Information (PII) to comply with privacy regulations. The data resides on an on- premises NetApp ONTAP cluster. The organization needs an automated tool to scan the data in- place and tag files containing PII without moving the data.
The project requirements are as follows:
Task: Identify PII in a large dataset.
Data_Location: On-premises ONTAP cluster.
Constraint: Data must not be moved from its source location for scanning.
Output: Tagged files containing PII.
Which NetApp tool is designed for this specific task?

A. NetApp XCP
B. NetApp SnapMirror
C. NetApp BlueXP classification
D. NetApp FlexCache

Answer: C

NEW QUESTION # 44
Which of the following best describes the difference between data lakes, data warehouses, and lakehouses?

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

Answer: A

NEW QUESTION # 45
An MLOps engineer is deploying a training pod that requires a high-performance volume. After applying the pod and PVC manifests, the pod remains in a 'Pending' state. The engineer runs
'kubectl describe pod training-pod-7d8c' and sees the following event:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 2m15s
default-scheduler 0/4 nodes are available: 1 node(s) had volume node affinity conflict, 3 node(s) didn't find available persistent volume to bind.
The engineer then inspects the associated PVC and sees its status is also 'Pending'.
What is the most likely cause of this issue?

A. The Kubernetes scheduler is malfunctioning and cannot assign pods to nodes.
B. The 'storageClassName' specified in the PVC does not match any existing StorageClass managed by Trident.
C. The Trident controller pod has crashed and needs to be restarted.
D. The ONTAP backend is out of available capacity to provision a new volume.

Answer: B

NEW QUESTION # 46
The firm decides to expand the "Advisor Assistant" project to a new team in a different department. This team needs its own isolated environment. An MLOps engineer attempts to submit a new GPU- intensive job for the new team, but it remains pending. The engineer checks the Run:AI scheduler logs and finds the following entry:
time="2025-07-11T16:45:00Z" level=info msg="Job ds-new-team-job1 cannot be scheduled.
Project 'new-team-project' has exceeded its GPU quota. Quota: 0, Requested: 1, Used: 0." What is the root cause of the scheduling failure?

A. The Run:AI scheduler is offline and cannot process new jobs.
B. A Run:AI project quota has been configured for the new team, but it has been set to zero, effectively blocking them from using any GPU resources.
C. The job is requesting a specific type of GPU that is not available in the cluster.
D. The Kubernetes cluster has no available GPUs.

Answer: B

NEW QUESTION # 47
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 analyzes data without generating content.
D. Generative AI can create new content such as text, images, and videos.

Answer: D

NEW QUESTION # 48
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