NS0-901 Latest Test Vce | NS0-901 Cert Exam

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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q38-Q43):

NEW QUESTION # 38
An AI architect is designing a storage solution for a new training cluster. The primary workload consists of training large language models, which involves sequential reads of massive datasets.
The key requirement is to maximize GPU utilization by providing the highest possible data throughput. Cost is a secondary concern to performance.
Which NetApp storage system is the most appropriate choice for this workload?

A. NetApp ASA (All-SAN Array)
B. NetApp StorageGRID
C. NetApp C-Series
D. NetApp E-Series

Answer: A

NEW QUESTION # 39
An architect is designing a fully automated, end-to-end MLOps pipeline on Kubernetes for a computer vision use case. The pipeline must handle everything from data versioning to model deployment.
The required pipeline stages are:
1. Data Versioning: Create a new, immutable version of the master dataset for the pipeline run.
2. Data Preparation: Launch a pod to run a preprocessing script on the versioned data.
3. Model Training: Launch a distributed training job that reads the prepared data from a highperformance volume.
4. Model Deployment: Push the trained model to a production inference service.
Which combination of NetApp and Kubernetes technologies provides the most effective and automated solution for this entire pipeline?

A. Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.
B. Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.
C. Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
D. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.
E. Use the NetApp DataOps Toolkit to create a Snapshot of the source data volume (for versioning), then create a FlexClone PVC from the snapshot for the preparation stage, and finally create a FlexGroup PVC for the training stage.

Answer: E

NEW QUESTION # 40
A team has deployed a Retrieval-Augmented Generation (RAG) system to answer customer queries. Recently, users have complained that the answers provided by the chatbot are outdated and do not reflect the latest product updates. An architect investigates and finds the following status log from the RAG pipeline's data ingestion monitor.
Timestamp: 2025-07-11T14:00:00Z
System: RAG Pipeline Monitor
Status: WARNING
Message: Vector DB freshness check failed.
Source data appears stale.
Vector_DB_Last_Update: 2025-06-10T08:00:00Z
Knowledge_Base_Last_Modified: 2025-07-11T13:15:00Z
Data_Sync_Service: BlueXP copy and sync
Sync_Job_Status: Succeeded
Based on the log, what is the most likely cause of the outdated answers?

A. The BlueXP copy and sync service is failing to copy data to the staging area.
B. The LLM needs to be fine-tuned with the new product information.
C. The process that converts staged documents into vectors and updates the vector database is not running.
D. The knowledge base itself has not been updated with the latest product information.

Answer: C

NEW QUESTION # 41
An AI platform is suffering from poor performance during distributed training jobs. The training data resides on a single, large NFS volume. Monitoring shows that while the overall network throughput to the storage system is high, individual GPU nodes experience significant I/O wait times, and the single ONTAP volume is becoming a performance bottleneck. The goal is to re- architect the storage layout to maximize read parallelism and throughput for the training cluster.
Which two actions should the architect take to address this performance bottleneck? (Choose 2.)

A. Use NetApp FlexCache to create a local cache of the training data on each compute node.
B. Replace the NFS protocol with iSCSI for all training data access.
C. Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.
D. Enable QoS maximums on the training volume to limit its IOPS.
E. Increase the number of network ports connected to the storage controller.

Answer: A,C

NEW QUESTION # 42
Which of the following applications use AI in the healthcare industry? (Choose two)

A. Diagnosing diseases
B. Automating financial reporting
C. Predicting patient outcomes
D. Managing hospital supply chains

Answer: A,C

NEW QUESTION # 43
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