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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
AI and Machine Learning Fundamentals	- Algorithm types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning - AI, ML, DL concepts 1. Training, inference, and prediction workflows 2. Differences between AI, machine learning, and deep learning
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
Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Autonomous systems and agents 3. Digital twins
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 (Q47-Q52):

NEW QUESTION # 47
Which storage protocols are commonly used for handling large-scale AI data? (Choose two)

A. Parallel file systems
B. File-based systems
C. POSIX-based file systems
D. Object-based storage

Answer: A,D

NEW QUESTION # 48
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 # 49
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. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
B. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
C. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
D. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
E. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
F. Use a standard 10GbE network for all connectivity to reduce costs.

Answer: B,C,D,E

NEW QUESTION # 50
The firm has acquired a competitor, and the volume of proprietary documents for the "Advisor Assistant" is expected to triple, exceeding the capacity of the current StorageGRID data lake. The architect needs to expand the data lake's capacity non-disruptively.
The current StorageGRID status is:
System_Health: Nominal
Node_Count: 6 (3 Storage Nodes, 3 Admin/Gateway Nodes)
Usable_Capacity: 1.5 PB
Used_Capacity: 1.4 PB (93%)
What is the standard procedure for scaling the capacity of the on-premises NetApp StorageGRID system?

A. Use FabricPool to tier the excess data from StorageGRID to a public cloud provider.
B. Replace the disks in the existing storage nodes with higher-capacity drives.
C. Provision a new, separate StorageGRID system and use BlueXP copy and sync to move the data.
D. Add new storage nodes to the existing StorageGRID system and execute an expansion procedure.

Answer: D

NEW QUESTION # 51
The pod running the vector database on the Kubernetes cluster fails to start. An MLOps engineer runs 'kubectl describe pod vector-db-pod-0' and sees the following event message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 30s default-scheduler 0/8 nodes are available: 8 node(s) did not match pod anti-affinity rules.
The pod's manifest contains the following 'affinity' definition:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app
operator: In
values:
- vector-db
topologyKey: "kubernetes.io/hostname"
What is the most likely reason the pod cannot be scheduled?

A. The PersistentVolumeClaim for the pod is in a 'Pending' state.
B. The 'topologyKey' is invalid; it should be 'failure-domain.beta.kubernetes.io/zone'.
C. The pod's anti-affinity rule prevents it from being scheduled on any node that is already running another pod with the label 'app=vector-db'.
D. The 'vector-db-pod-0' is requesting more GPU resources than are available on any node.

Answer: C

NEW QUESTION # 52
......

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