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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

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

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

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

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

5. A research lab uses a fleet of autonomous drones to collect high-resolution aerial imagery for agricultural analysis. The drones land at a remote edge location and offload their data. The AI models for image analysis are trained at a central data center. The team is using NetApp SnapMirror to replicate the data from the edge to the core. However, the data scientists are complaining that the datasets arriving at the data center are often incomplete or corrupted.
An administrator reviews the SnapMirror configuration and status via the BlueXP API:
{
"source": { "workingEnvironmentId": "OnPrem-Edge-Filer-1", "volumeName": "drone_data_raw" },
"destination": { "workingEnvironmentId": "Core-Datacenter-A800", "volumeName":
"drone_data_replicated" },
"mirrorState": "broken-off",
"relationshipStatus": "idle",
"unhealthyReason": "Transfer failed. Destination volume is out of space.", "lastTransferInfo": {
"transferError": "No space left on device"
}
}
What is the direct cause of the incomplete datasets at the data center?

A) The source volume at the edge location has become corrupted.
B) The SnapMirror relationship is broken because the destination volume at the core data center has run out of capacity.
C) The network connection between the edge and the core is unreliable.
D) The BlueXP Connector does not have the correct permissions to manage the SnapMirror relationship.

Solutions: