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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

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

2. 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) Reinforcement learning
C) Supervised learning
D) Generative learning

3. An organization has a core data center with a large AI training cluster and several remote edge locations for data ingest and local inference. The edge locations frequently need access to the latest models trained in the core data center, but WAN bandwidth is limited and can be unreliable.
Users at the edge are reporting slow model loading times.
An architect reviews the data access logs from an edge site:
Timestamp: 2025-07-11T15:30:00Z
Event: Model_Load_Request
Model_Path: nfs://core-filer.example.com/vol/models/latest_model.pkl
Source_IP: 192.168.100.15 (Edge Server)
Destination_IP: 10.1.1.50 (Core Filer)
Status: SUCCESS
Duration: 3600s (60 minutes)
What is the most likely cause of the slow model loading times at the edge?

A) The core ONTAP filer is using slow, capacity-based disks.
B) The edge server does not have enough RAM to cache the model effectively.
C) The model file is being transferred over a slow, high-latency WAN link for every load request.
D) The NFS version used between the core and edge is outdated.

4. A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)

A) NetApp FlexCache
B) NetApp StorageGRID
C) NetApp SnapMirror
D) NetApp XCP
E) NetApp FabricPool

5. An architect is designing a scalable, automated MLOps platform using Kubeflow on a Kubernetes cluster. The platform must support the entire AI lifecycle for multiple teams, with different storage requirements at each stage.
The key requirements are:
- Data Ingestion: A pipeline step needs a shared, read-write volume accessible by multiple pods to stage raw data.
- Experimentation: Data scientists need individual, isolated volumes for their Jupyter notebooks.
- Training: Distributed training jobs require a high-performance, parallel-access filesystem for reading training data.
- Automation: All storage must be provisioned automatically via Kubeflow pipeline definitions without manual intervention.
Which combination of technologies and configurations would create the most effective solution?

A) Rely on hostPath volumes for all storage to ensure the highest performance.
B) Create a single, large NFS volume and mount it to all pods using a static PersistentVolume.
C) Use the NetApp DataOps Toolkit for Python within the Kubeflow pipeline components to dynamically create and manage Trident PVCs for each stage.
D) Configure multiple Trident backends (e.g., 'ontap-nas' for standard volumes, 'ontap-nas-flexgroup' for parallel access) and corresponding StorageClasses.
E) Use the NetApp DataOps Toolkit for all storage provisioning, bypassing Trident and Kubernetes PVCs.

Solutions: