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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
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 Common Challenges	22%	- Traceability and Optimization 1. Optimizing data access and movement 2. Maximizing performance in demanding AI workloads 3. Ensuring traceability for code, data, and models - Resource Management 1. Sizing storage and compute resources effectively 2. Controlling costs and securing storage
AI Lifecycle	27%	- Generative AI Concepts 1. Hallucinations 2. Retrieval Augmented Generation (RAG) 3. Fine-tuning - 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. Data aggregation and cleansing 3. XCP and CopySync - Model Development 1. Inferencing 2. Model building 3. Fine-tuning workflows
AI Hardware Architectures	18%	- NetApp Architectures 1. OVX architectures 2. BasePod 3. SuperPOD - Networking and Storage 1. Network protocols for AI workloads 2. Storage architectures for AI - Infrastructure Topologies 1. Data aggregation and compute topologies
AI Overview	15%	- AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - AI Deployment Models 1. Edge 2. On-premises 3. Benefits and risks of each model 4. Cloud - Algorithm Types 1. Unsupervised learning 2. Reinforcement learning 3. Supervised learning - Machine Learning Fundamentals 1. Describe machine learning benefits 2. Understand the relationship between AI, machine learning, and deep learning - AI Industry Applications 1. Healthcare applications 2. Agents 3. Digital twins

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q79-Q84):

NEW QUESTION # 79
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. Enable QoS maximums on the training volume to limit its IOPS.
C. Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.
D. Replace the NFS protocol with iSCSI for all training data access.
E. Increase the number of network ports connected to the storage controller.

Answer: A,C

NEW QUESTION # 80
The company's finance department mandates a cost-control strategy for the petabyte-scale StorageGRID data lake. The analysis shows that 80% of the simulation data is not accessed after
90 days but must be retained for five years for regulatory compliance. Which StorageGRID feature should the architect use to automatically reduce the long-term storage costs for this inactive data?

A. Enable deduplication and compression on all StorageGRID storage nodes.
B. Configure an Information Lifecycle Management (ILM) policy that automatically moves objects older than 90 days to a lower-cost cloud archive tier, such as Amazon S3 Glacier Deep Archive.
C. Implement a FabricPool policy to tier the data to a NetApp ASA system.
D. Use NetApp SnapMirror to replicate the cold data to a different, lower-cost StorageGRID cluster.

Answer: B

NEW QUESTION # 81
Advisors report that some queries to the chatbot are unacceptably slow, taking several seconds to respond. The MLOps team isolates the issue to the RAG retrieval step. Performance monitoring of the NetApp AFF A-Series hosting the vector database shows the following metrics during periods of high query load.
avg_read_latency: 3500 microseconds (3.5 ms)
avg_write_latency: 400 microseconds (0.4 ms)
iops_total: 15,000
cpu_utilization_storage_node: 15%
workload_profile: 95% small, random reads
Given these metrics, what is the most likely performance bottleneck?

A. The network between the application and the storage system is saturated.
B. The storage node CPU is the bottleneck, preventing faster data processing.
C. The AFF A-Series system is underpowered and cannot handle the IOPS load.
D. The vector database is poorly indexed, causing inefficient, slow lookups that translate to high read latency on the storage.

Answer: D

NEW QUESTION # 82
A robotics company is developing a control system for an autonomous warehouse drone. The drone must learn to navigate complex environments to pick up packages. The development team has created a physics-based simulation where the drone can attempt the task millions of times.
The drone receives a positive reward for successfully retrieving a package and a negative penalty for collisions. Which type of machine learning algorithm is being used in this scenario?

A. Unsupervised learning
B. Supervised learning
C. Generative learning
D. Reinforcement learning

Answer: D

NEW QUESTION # 83
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 process that converts staged documents into vectors and updates the vector database is not running.
C. The knowledge base itself has not been updated with the latest product information.
D. The LLM needs to be fine-tuned with the new product information.

Answer: B

NEW QUESTION # 84
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