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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. Reinforcement learning 2. Supervised learning 3. Unsupervised learning
AI Infrastructure and NetApp Solutions	- AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads - Converged workloads 1. AI, HPC, and analytics convergence 2. Shared infrastructure design considerations
AI Lifecycle and Deployment	- End-to-end AI lifecycle 1. Data ingestion and preparation 2. Model training and validation 3. Model deployment and monitoring - Operational challenges 1. Data governance and management 2. Scalability and performance optimization
Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Autonomous systems and agents 3. Digital twins

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q58-Q63):

NEW QUESTION # 58
An AI architect is reviewing the design for a new data lake. The primary requirement is to store petabytes of unstructured data (images, video, sensor logs) in a highly durable, scalable, and cost- effective manner. The data will be accessed via S3 API by various data processing and analytics applications.
The initial design proposes using a traditional Network Attached Storage (NAS) filer with a large number of disks. The architect reviews the proposal:
Proposed_System: Traditional NAS Filer
Protocol: NFSv4
Scalability_Model: Scale-up
Metadata_Handling: Centralized in filer head
Cost_per_GB: Moderate
Why is this proposed system a poor choice for a petabyte-scale data lake?

A. NFS is incapable of storing image or video files.
B. A NAS filer cannot be deployed on-premises.
C. A traditional scale-up NAS system will face scalability and cost-effectiveness challenges at the petabyte scale compared to an object storage system.
D. The S3 API cannot be used to access data stored on an NFS file system.

Answer: C

NEW QUESTION # 59
An AI infrastructure architect is tasked with designing a solution to address two critical challenges in a large, multi-petabyte AI environment:
1. Cost: A significant portion of the data on the high-performance all-flash storage is inactive but must remain online. The cost of storing this cold data on the performance tier is prohibitive.
2. Traceability: Data scientists need a simple, space-efficient way to version their datasets at key points in their workflow to ensure reproducibility.
The environment consists of NetApp AFF A-Series and NetApp StorageGRID systems.
Which combination of NetApp technologies should the architect implement to solve both challenges simultaneously? (Select all that apply.)

A. Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.
B. Use NetApp XCP to periodically move cold data from the AFF systems to StorageGRID.
C. Use BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.
D. Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.
E. Implement NetApp FlexClone to create full, writable copies of datasets for each experiment.
F. Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.

Answer: A,F

NEW QUESTION # 60
Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)

A. Horizontally scale the cluster by adding new GPU-equipped nodes.
B. Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
C. Replace all existing GPUs with the next-generation model to increase the performance of each node.
D. Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.
E. Manually deploy all new application replicas to a single, new, very large "super-node".

Answer: A,D

NEW QUESTION # 61
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 two distinct StorageClasses, 'gold-tier' and 'bronze-tier'.
B. Create a single StorageClass named 'multi-tier-storage' that references both backends.
C. Configure the 'bronze-tier' StorageClass to reference the StorageGRID backend.
D. Create a single Trident backend that points to both the ASA and StorageGRID systems simultaneously.
E. Create two separate Trident backend configurations, one for the ASA ('backend-asa.yaml') and one for StorageGRID ('backend-sgrid.yaml').
F. Configure the 'gold-tier' StorageClass to reference the ASA backend.

Answer: A,C,E,F

NEW QUESTION # 62
An MLOps engineer is deploying a training pod that requires a high-performance volume. After applying the pod and PVC manifests, the pod remains in a 'Pending' state. The engineer runs
'kubectl describe pod training-pod-7d8c' and sees the following event:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 2m15s
default-scheduler 0/4 nodes are available: 1 node(s) had volume node affinity conflict, 3 node(s) didn't find available persistent volume to bind.
The engineer then inspects the associated PVC and sees its status is also 'Pending'.
What is the most likely cause of this issue?

A. The 'storageClassName' specified in the PVC does not match any existing StorageClass managed by Trident.
B. The ONTAP backend is out of available capacity to provision a new volume.
C. The Kubernetes scheduler is malfunctioning and cannot assign pods to nodes.
D. The Trident controller pod has crashed and needs to be restarted.

Answer: A

NEW QUESTION # 63
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