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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
AI and Machine Learning Fundamentals	- AI, ML, DL concepts 1. Training, inference, and prediction workflows 2. Differences between AI, machine learning, and deep learning - Algorithm types 1. Unsupervised learning 2. Reinforcement learning 3. Supervised learning
AI Infrastructure and NetApp Solutions	- Converged workloads 1. AI, HPC, and analytics convergence 2. Shared infrastructure design considerations - AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads
Industry Use Cases	- AI applications across industries 1. Autonomous systems and agents 2. Digital twins 3. Healthcare AI
AI Lifecycle and Deployment	- End-to-end AI lifecycle 1. Data ingestion and preparation 2. Model deployment and monitoring 3. Model training and validation - Operational challenges 1. Scalability and performance optimization 2. Data governance and management

Network Appliance NetApp Certified AI Expert Sample Questions:

Question #1

An organization's AI platform team needs to provide two distinct tiers of storage for their data scientists on a single Kubernetes cluster:
1. 'gold-tier': Extremely low-latency storage for active model training, using an all-flash NetApp ASA system.
2. 'bronze-tier': Cost-effective, high-capacity storage for data staging and archiving, using a NetApp StorageGRID system.
How should the platform team configure NetApp Trident to meet these requirements? (Select all that apply.)

A. Create a single Trident backend that points to both the ASA and StorageGRID systems simultaneously.
B. Create a single StorageClass named 'multi-tier-storage' that references both backends.
C. Configure the 'gold-tier' StorageClass to reference the ASA backend.
D. Create two distinct StorageClasses, 'gold-tier' and 'bronze-tier'.
E. Create two separate Trident backend configurations, one for the ASA ('backend-asa.yaml') and one for StorageGRID ('backend-sgrid.yaml').
F. Configure the 'bronze-tier' StorageClass to reference the StorageGRID backend.

Reveal Solution Discussion 0

Correct Answer: C,D,E,F 🗳️

Question #2

A national research laboratory is investing in a turnkey AI infrastructure solution. Their primary goal is to eliminate the complexity and risk of designing and integrating the compute, network, and storage components themselves. The solution must be pre-validated by the vendors to deliver predictable, linear performance as they scale from one to multiple compute nodes. Which two options represent this type of pre-validated, converged infrastructure solution for AI? (Choose two)

A. A single NetApp FAS system connected directly to a single server.
B. A public cloud provider's general-purpose virtual machine instances.
C. A Cisco and NetApp FlexPod for AI solution.
D. A custom-built server rack with components sourced from various different vendors.
E. NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking.

Reveal Solution Discussion 0

Correct Answer: C,E 🗳️

Question #3

An AI infrastructure engineer is troubleshooting a poorly performing distributed training job. The job is running across multiple nodes, each equipped with powerful GPUs. The engineer observes that overall GPU utilization is unexpectedly low. System-level monitoring on the compute nodes provides the following metrics during a training run.
avg_gpu_utilization: 25%
avg_cpu_iowait_percent: 65%
avg_network_bandwidth_util: 95% (on a 10GbE network)
storage_array_latency: <1ms
Given these metrics, what is the most likely bottleneck causing the low GPU utilization?

A. The network connecting the compute nodes and storage is saturated and has become the primary bottleneck.
B. The CPU is underpowered and cannot preprocess the data fast enough for the GPUs.
C. The storage array is too slow and cannot serve data quickly enough.
D. The GPUs are faulty and cannot process data at their rated speed.

Reveal Solution Discussion 0

Correct Answer: A 🗳️

Question #4

An AI team is embarking on a project to train a new, large-scale computer vision model from scratch. The lead architect emphasizes that the success of the project depends on four fundamental inputs that must be available and managed throughout the training process. Which of the following are the four essential requirements for model generation?

A. A pre-trained model, a validation set, an inference engine, and a cloud provider.
B. A data lake, a data warehouse, a data pipeline, and a data mart.
C. Data, code, compute, and time.
D. A project manager, a data scientist, a software engineer, and a budget.

Reveal Solution Discussion 0

Correct Answer: C 🗳️

Question #5

An architect is designing a cost-optimized storage solution for a large AI environment that has two distinct data temperature profiles:
1. Hot Data (200 TB): Actively used training sets and models requiring all-flash performance.
2. Cold Data (2 PB): Inactive, archived datasets and old model versions that must remain online but are infrequently accessed.
The solution must automatically manage data placement based on activity, without requiring manual intervention or changes to file paths. The environment consists of an on-premises data center and a public cloud account.
Which combination of NetApp technologies provides the most complete and cost-effective solution? (Select all that apply.)

A. Use NetApp XCP to manually script the daily movement of cold files from the AFF system to StorageGRID.
B. Deploy a NetApp AFF A-Series system on-premises to serve as the high-performance tier for the hot data.
C. Deploy a single, large Cloud Volumes ONTAP instance to hold both the hot and cold data tiers.
D. Deploy NetApp StorageGRID on-premises to serve as a cost-effective capacity tier for the cold data.
E. Use NetApp SnapMirror to create a full copy of the 2 PB of cold data on the AFF A-Series for faster access if needed.
F. Use NetApp FabricPool to automatically and transparently tier cold data blocks from the AFF A- Series to the StorageGRID system.