Network Appliance NS0-901 real exam prep : NetApp Certified AI Expert Exam

An architect is designing a data pipeline for a predictive AI model that will forecast retail sales.
The pipeline must be robust, version-controlled, and efficient.
The proposed data flow is as follows:
1. Ingest: Raw sales data is copied daily from multiple point-of-sale (POS) systems to a central staging area on an on-premises ONTAP cluster.
2. Prepare: The raw data is messy. A data engineering team needs a clean, isolated, and writable copy of the latest daily data to perform cleansing and feature engineering tasks without impacting the original raw data.
3. Train: Once prepared, the cleansed dataset is used to retrain the predictive model on a GPU cluster.
This step must be repeatable with the exact same dataset for compliance.
4. Deploy: The newly trained model is pushed to production inference servers.
Which combination of NetApp technologies best supports this entire predictive AI lifecycle?
(Select all
that apply.)

• A. Use NetApp XCP to efficiently aggregate the raw sales data from POS systems into the central staging area.
• B. Use NetApp StorageGRID as the primary storage for the high-performance training stage.
• C. Use NetApp FlexClone to create an instantaneous, space-efficient, writable copy of the daily raw data for the data preparation stage.
• D. Use BlueXP backup and recovery to perform the initial data ingest from the POS systems.
• E. Use a RAG architecture for the sales forecasting model.
• F. Use NetApp Snapshots on the prepared dataset volume just before training to create an immutable, point-in-time version for compliance and reproducibility.

What is the primary reason for implementing a hybrid cloud architecture in this medical imaging scenario?

• A. To reduce the overall cost of storage by keeping all data in the public cloud.
• B. To simplify network management by using a single, global virtual network spanning the on- premises and cloud environments.
• C. To exclusively use cloud-native AI services, eliminating the need for any on-premises hardware.
• D. To leverage the powerful, elastic GPU compute resources available in the public cloud for model training while keeping sensitive source data on-premises to meet security and HIPAA compliance requirements.

To meet HIPAA compliance, the first step in the data pipeline is to identify all medical scans that contain embedded PII. The solution must be automated and capable of scanning data in-place on the on-premises ASA system.
Which two technologies should be used to accomplish this identification and tagging task?
(Choose 2.)

• A. NetApp SnapLock to make the source data immutable before scanning.
• B. A custom "PII" category within BlueXP classification to identify specific medical record number formats.
• C. NetApp BlueXP classification, configured to scan the on-premises ASA working environment.
• D. A custom Python script that uses regular expressions to search file contents.
• E. A manual review process where technicians visually inspect each scan for PII.

A pharmaceutical company is creating a "digital twin" of its manufacturing process. This involves running complex simulations (an HPC workload) that generate massive datasets.
The company wants to use this data immediately for two other purposes:
1. Analytics: Business analysts need to run complex queries on the simulation output using tools like Spark.
2. AI Training: Data scientists need to use the same output as a training set for a predictive maintenance model.
The company wants to avoid creating separate data silos for each workload.
Which two NetApp technologies are best suited for building a unified data lake that can efficiently serve all three workloads (HPC, Analytics, AI)? (Choose 2.)

• A. NetApp ONTAP with FlexGroup volumes to provide high-throughput, parallel NFS access for the HPC and AI training workloads.
• B. NetApp Keystone to provide a flexible, pay-as-you-go consumption model for the infrastructure.
• C. NetApp SnapCenter to create application-consistent backups of the data.
• D. NetApp Autonomous Ransomware Protection to secure the data from modification.
• E. NetApp StorageGRID to provide a scalable, S3-native object store that integrates directly with modern analytics platforms like Spark.

What is the primary difference between C-Series and A-Series storage for AI workloads?

• A. C-Series supports both file and object-based storage, while A-Series is optimized for object storage.
• B. A-Series is for smaller-scale AI workloads, while C-Series supports large-scale, high-performance AI applications.
• C. A-Series provides GPU-based acceleration, while C-Series only supports CPU-based workloads.
• D. C-Series is designed for high-performance workloads, while A-Series focuses on cost-efficiency for general storage.