NS0-901 Certification Sample Questions | NS0-901 Exam

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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Cloud and Hybrid Cloud AI Deployment	18%	- Hybrid and multi-cloud AI architectures - Cloud-native AI solutions and integration - NetApp cloud data services for AI - Data mobility and consistency across environments
Topic 2: AI Lifecycle	27%	- Predictive vs generative AI - Model training, inference, and optimization - Data preparation and management for AI - AI lifecycle stages: design, training, deployment, monitoring - AI governance, ethics, and compliance
Topic 3: Security, Reliability, and Operations	15%	- High availability and data protection - Cost management and efficiency - Data security and access control for AI - Monitoring, logging, and troubleshooting AI environments
Topic 4: NetApp AI Solutions and Architecture	25%	- ONTAP integration with AI frameworks - Data management and data pipeline design - NetApp AI-ready infrastructure components - Scalability and performance optimization for AI - Storage architectures for AI workloads
Topic 5: AI Overview	15%	- AI deployment models: on-premises, cloud, edge - AI industry use cases and applications - Algorithm types: supervised, unsupervised, reinforcement learning - AI, machine learning, and deep learning concepts - Convergence of AI, high-performance computing, and analytics

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q23-Q28):

NEW QUESTION # 23
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 'requiredTopology' in the StorageClass is misconfigured, preventing Trident from finding a valid ONTAP backend in the same zone as the available compute nodes.
B. The training pod's container image does not exist in the registry.
C. The Kubernetes nodes do not have the necessary NFS client utilities installed.
D. The NetApp ASA storage system is offline.

Answer: A

NEW QUESTION # 24
In the data pipeline, which NetApp technology is used to create an efficient, block-level, incremental replication of the anonymized datasets from the on-premises ASA to the Cloud Volumes ONTAP instance in Azure?

A. NetApp SnapMirror
B. NetApp BlueXP copy and sync
C. NetApp XCP
D. NetApp FabricPool

Answer: A

NEW QUESTION # 25
Which of the following best describes the difference between data lakes, data warehouses, and lakehouses?

A. Data lakes store structured data, data warehouses store unstructured data, and lakehouses store only real-time data.
B. Data lakes store raw, unstructured data, data warehouses store structured data, and lakehouses combine the features of both.
C. Data lakes store metadata, data warehouses store transaction data, and lakehouses store archival data.
D. Data lakes store data in cloud storage, data warehouses store it in traditional databases, and lakehouses store it in external drives.

Answer: B

NEW QUESTION # 26
A financial services company has deployed a real-time fraud detection model at the edge. The model is designed for low-latency inference. However, monitoring reports indicate that the infrastructure costs are excessively high, and GPU utilization is consistently low. The architect reviews the deployment configuration.
Instance_Type: NVIDIA DGX A100 (8 GPUs)
Storage_Tier: High-Performance All-Flash (NetApp ASA)
Network: 100GbE RoCE
GPU_Utilization_Avg: 5%
Monthly_Cost: $15,000
Workload_Profile: Low-volume, sporadic, real-time predictions
What is the most likely cause of the high costs and low utilization?

A. The storage tier is too slow, causing the GPUs to wait for data.
B. The network latency is too high for an edge deployment.
C. The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.
D. The model was trained using supervised learning, which is inefficient for fraud detection.

Answer: C

NEW QUESTION # 27
An architect is designing the storage and network infrastructure for a new, large-scale AI cluster dedicated to training foundational models. The primary design goal is to achieve the highest possible data throughput and the lowest latency to ensure multi-million dollar GPU resources are never idle. Which two technologies are essential to include in the design to achieve this goal?
(Choose 2.)

A. An InfiniBand or RoCE-capable Ethernet network fabric.
B. GPUDirect Storage support on the storage system.
C. NetApp StorageGRID as the primary storage for the training datasets.
D. A tiered storage architecture using NetApp FabricPool.
E. A 10GbE Ethernet network for all data traffic.

Answer: A,B

NEW QUESTION # 28
......

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