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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Hardware Architectures	18%	- Infrastructure Topologies 1. Data aggregation and compute topologies - NetApp Architectures 1. SuperPOD 2. BasePod 3. OVX architectures - Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads
Topic 2: AI Common Challenges	22%	- Resource Management 1. Sizing storage and compute resources effectively 2. Controlling costs and securing storage - Traceability and Optimization 1. Maximizing performance in demanding AI workloads 2. Optimizing data access and movement 3. Ensuring traceability for code, data, and models
Topic 3: AI Overview	15%	- AI Industry Applications 1. Agents 2. Digital twins 3. Healthcare applications - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - AI Deployment Models 1. Edge 2. Benefits and risks of each model 3. On-premises 4. Cloud - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - Algorithm Types 1. Reinforcement learning 2. Supervised learning 3. Unsupervised learning - Machine Learning Fundamentals 1. Understand the relationship between AI, machine learning, and deep learning 2. Describe machine learning benefits
Topic 4: AI Lifecycle	27%	- Generative AI Concepts 1. Hallucinations 2. Fine-tuning 3. Retrieval Augmented Generation (RAG) - Predictive AI vs. Generative AI 1. Large Language Models (LLMs) 2. Distinction between predictive and generative AI 3. Impact of generative content (text, images, video, decision-making) - Model Development 1. Fine-tuning workflows 2. Inferencing 3. Model building - Data Preparation 1. Data aggregation and cleansing 2. NetApp BlueXP Classification 3. XCP and CopySync
Topic 5: AI Software Architectures	18%	- Scaling and Orchestration 1. Leveraging BlueXP software tools 2. Scaling AI workloads with Kubernetes - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale - Development Tools 1. NetApp DataOps Toolkit 2. Jupyter notebooks vs. pipelines

Network Appliance NetApp Certified AI Expert Sample Questions:

Question 1

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

Question 2

A training job on one of the NVIDIA DGX servers is running slowly. A performance engineer runs the 'dstat' command on the server and captures the following output during the job execution.
-total-cpu-usage- -dsk/total- -net/total- paging-- system-- usr sys idl wai hiq siq| read writ| recv send| in out | int csw 15 5 70 10 0 0| 1.2G 15.0M| 1.2G 12.0M| 0 0 | 15k 35k
14 6 69 11 0 0| 1.2G 14.3M| 1.2G 11.8M| 0 0 | 14k 33k
16 5 68 11 0 0| 1.2G 16.1M| 1.2G 13.1M| 0 0 | 16k 36k
The server is connected to the NetApp ASA via a 100GbE (12.5 GB/s) network.
What is the most likely performance bottleneck based on this data?

A. The server's CPU is overloaded.
B. The storage system (ASA) is unable to provide sufficient read throughput.
C. The application is experiencing excessive memory paging.
D. The server's network interface is saturated.

Question 3

The company decides to establish a disaster recovery (DR) site in a secondary data center for the entire Digital Twin platform. The DR plan must protect the HPC data, the AI training data, and the central data lake.
The DR requirements are:
- RPO: 4 hours for all data.
- RTO: 24 hours for the entire platform.
- Process: The failover and failback process should be as automated as possible.
Which combination of technologies provides the most comprehensive DR solution?

A. Use NetApp SnapMirror for the ASA, StorageGRID replication for the data lake, and rely on tape backups for the E-Series/BeeGFS data.
B. Deploy a MetroCluster configuration spanning the primary and DR sites for all three storage systems.
C. Use NetApp SnapMirror for the ASA, StorageGRID's built-in replication for the data lake, and a host- based tool like 'rsync' for the E-Series/BeeGFS data, all orchestrated via BlueXP disaster recovery.
D. Use BlueXP backup and recovery to back up all three storage systems (ASA, E-Series, StorageGRID) to a single cloud target.

Question 4

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

Question 5

The pod running the vector database on the Kubernetes cluster fails to start. An MLOps engineer runs 'kubectl describe pod vector-db-pod-0' and sees the following event message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 30s default-scheduler 0/8 nodes are available: 8 node(s) did not match pod anti-affinity rules.
The pod's manifest contains the following 'affinity' definition:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app
operator: In
values:
- vector-db
topologyKey: "kubernetes.io/hostname"
What is the most likely reason the pod cannot be scheduled?

A. The PersistentVolumeClaim for the pod is in a 'Pending' state.
B. The 'topologyKey' is invalid; it should be 'failure-domain.beta.kubernetes.io/zone'.
C. The pod's anti-affinity rule prevents it from being scheduled on any node that is already running another pod with the label 'app=vector-db'.
D. The 'vector-db-pod-0' is requesting more GPU resources than are available on any node.

Solutions:

Question 1
Answer: A

Question 2
Answer: D

Question 3
Answer: C

Question 4
Answer: D

Question 5
Answer: C