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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%	- Infrastructure Topologies 1. Data aggregation and compute topologies - Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads - NetApp Architectures 1. BasePod 2. OVX architectures 3. SuperPOD
AI Lifecycle	27%	- Predictive AI vs. Generative AI 1. Large Language Models (LLMs) 2. Impact of generative content (text, images, video, decision-making) 3. Distinction between predictive and generative AI - Data Preparation 1. NetApp BlueXP Classification 2. Data aggregation and cleansing 3. XCP and CopySync - Generative AI Concepts 1. Retrieval Augmented Generation (RAG) 2. Hallucinations 3. Fine-tuning - Model Development 1. Model building 2. Inferencing 3. Fine-tuning workflows
AI Overview	15%	- Algorithm Types 1. Reinforcement learning 2. Supervised learning 3. Unsupervised 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. Digital twins 3. Agents - 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 Deployment Models 1. Cloud 2. Benefits and risks of each model 3. On-premises 4. Edge
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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q103-Q108):

NEW QUESTION # 103
An AI architect needs to design a complete, end-to-end data pipeline for a new generative AI application at a financial services firm. The application will allow internal analysts to query a massive, 500 TB archive of historical market data and reports to generate summaries. The firm has the following environment and requirements:
Data_Sources: A mix of on-premises ONTAP filers and StorageGRID S3 buckets.
Requirement_1: All queries must be answered using only the private data archive.
Requirement_2: All generated summaries must provide citations to the source reports.
Requirement_3: All data containing client PII must be identified and excluded from the LLM context.
Requirement_4: The solution must be cost-effective for the large, mostly-read data archive.
Which set of actions and technologies constitutes the most robust and compliant solution?
(Select all that apply.)

A. Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.
B. Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
C. During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.
D. Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
E. Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.
F. Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.

Answer: B,C,E,F

NEW QUESTION # 104
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. 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.
B. Enable deduplication and compression on all StorageGRID storage nodes.
C. Use NetApp SnapMirror to replicate the cold data to a different, lower-cost StorageGRID cluster.
D. Implement a FabricPool policy to tier the data to a NetApp ASA system.

Answer: A

NEW QUESTION # 105
A data scientist is using the NetApp DataOps Toolkit for Python to automate the creation of a new, writable volume for an experiment. The script is intended to clone an existing dataset volume. When the script is executed, it fails with an error.
The relevant portion of the Python script is:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="dataset-v1-pvc",
new_pvc_name="experiment-clone-pvc",
namespace="ds-team-1"
)
The script produces the following error in the terminal:
'Error: Failed to clone PVC. Source PVC 'dataset-v1-pvc' not found in namespace 'ds-team-1'.' What is the most likely cause of this error?

A. The NetApp DataOps Toolkit does not support cloning volumes.
B. The Kubernetes cluster does not have NetApp Trident installed.
C. The source PersistentVolumeClaim (PVC) named 'dataset-v1-pvc' does not exist or is in a different namespace.
D. The Python script is missing the necessary import statement for the toolkit.

Answer: C

NEW QUESTION # 106
A data science team works primarily at a central data center but needs to run a short-term, burst- compute training job in the public cloud to take advantage of specialized GPUs that are not available on- premises. They need to efficiently and securely move a 20 TB dataset from their on- premises ONTAP cluster to a Cloud Volumes ONTAP instance for the duration of the job.
The data flow requirement is as follows:
Source: On-premises ONTAP cluster
Destination: Cloud Volumes ONTAP in AWS
Requirement: Efficient, secure, block-level data transfer.
Which NetApp technology is the most appropriate tool for this task?

A. NetApp XCP
B. NetApp FabricPool
C. NetApp SnapMirror
D. NetApp FlexCache

Answer: C

NEW QUESTION # 107
An AI infrastructure architect is tasked with designing a solution to address two critical challenges in a large, multi-petabyte AI environment:
1. Cost: A significant portion of the data on the high-performance all-flash storage is inactive but must remain online. The cost of storing this cold data on the performance tier is prohibitive.
2. Traceability: Data scientists need a simple, space-efficient way to version their datasets at key points in their workflow to ensure reproducibility.
The environment consists of NetApp AFF A-Series and NetApp StorageGRID systems.
Which combination of NetApp technologies should the architect implement to solve both challenges simultaneously? (Select all that apply.)

A. Use NetApp XCP to periodically move cold data from the AFF systems to StorageGRID.
B. Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.
C. Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.
D. Implement NetApp FlexClone to create full, writable copies of datasets for each experiment.
E. Use BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.
F. Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.

Answer: B,F

NEW QUESTION # 108
......

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