Valid NS0-901 Exam Materials, NS0-901 Free Sample

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: 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
Topic 2: AI and Machine Learning Fundamentals	- AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows - Algorithm types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning
Topic 3: Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Digital twins 3. Autonomous systems and agents
Topic 4: 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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q10-Q15):

NEW QUESTION # 10
An AI platform team is investigating poor I/O performance for a specific workload that involves processing hundreds of thousands of small metadata files. The application is running on a Kubernetes cluster with storage provided by a NetApp ONTAP system over NFS. Performance metrics show acceptable network throughput but very high latency for metadata operations (e.g., open, stat, close).
The current storage configuration is as follows:
Storage_System: NetApp AFF A-Series
Protocol: NFSv4.1
Workload_Profile: Metadata-intensive, many small file lookups
Observed_Issue: High latency on metadata operations, slow job completion Which storage architecture would be better suited to handle this specific metadata-intensive workload?

A. A NetApp StorageGRID object storage system.
B. A NetApp E-Series system running a parallel file system like BeeGFS.
C. A NetApp SnapLock-enabled volume for data protection.
D. A NetApp Cloud Volumes ONTAP instance in a different region.

Answer: B

NEW QUESTION # 11
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. Deploy a MetroCluster configuration spanning the primary and DR sites for all three storage systems.
B. Use BlueXP backup and recovery to back up all three storage systems (ASA, E-Series, StorageGRID) to a single cloud target.
C. Use NetApp SnapMirror for the ASA, StorageGRID replication for the data lake, and rely on tape backups for the E-Series/BeeGFS data.
D. 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.

Answer: D

NEW QUESTION # 12
A distributed training job running on the AIPod fails to start. The MLOps engineer inspects the events for one of the pending training pods and sees the following message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 5m12s default-scheduler 0/4 nodes are available: 4 node(s) had no available volume zone.
The PersistentVolumeClaim (PVC) for this pod specifies a StorageClass that uses the 'ontap-nas' Trident provisioner.
he Trident logs show no errors.
What is the most likely cause of this scheduling failure?

A. The Kubernetes nodes do not have the necessary NFS client utilities installed.
B. The 'requiredTopology' in the StorageClass is misconfigured, preventing Trident from finding a valid ONTAP backend in the same zone as the available compute nodes.
C. The NetApp ASA storage system is offline.
D. The training pod's container image does not exist in the registry.

Answer: B

NEW QUESTION # 13
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. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
D. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
E. Use a standard 10GbE network for all connectivity to reduce costs.
F. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.

Answer: A,B,C,F

NEW QUESTION # 14
Which of the following are typical requirements for inferencing in AI models? (Choose two)

A. Pre-trained models
B. Loading the model into memory
C. Extensive training data
D. Cloud-based inference only

Answer: A,B

NEW QUESTION # 15
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