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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
An architect is explaining different data storage paradigms to a new data engineering team. The team needs to understand where to store different types of data for their AI projects. Which statement accurately describes a key difference between a data lake and a data warehouse?

A. A data lake is designed to store vast quantities of raw, multi-format data (structured, semi- structured, and unstructured), while a data warehouse is typically used for storing structured and filtered data that has already been processed for a specific purpose.
B. A data lake is only for on-premises deployments, while a data warehouse is only for cloud deployments.
C. A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.
D. A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.

Answer: A

NEW QUESTION # 18
Which of the following applications use AI in the healthcare industry? (Choose two)

A. Predicting patient outcomes
B. Automating financial reporting
C. Diagnosing diseases
D. Managing hospital supply chains

Answer: A,C

NEW QUESTION # 19
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 model was trained using supervised learning, which is inefficient for fraud detection.
C. The compute and storage infrastructure is sized for a large-scale training workload, not a lightweight inference workload.
D. The network latency is too high for an edge deployment.

Answer: C

NEW QUESTION # 20
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. NetApp StorageGRID as the primary storage for the training datasets.
B. A 10GbE Ethernet network for all data traffic.
C. A tiered storage architecture using NetApp FabricPool.
D. An InfiniBand or RoCE-capable Ethernet network fabric.
E. GPUDirect Storage support on the storage system.

Answer: D,E

NEW QUESTION # 21
The data anonymization job, running in a Kubernetes pod, fails. The pod logs show a "Permission Denied" error when trying to access the source volume on the on-premises ASA. An administrator checks the export policy rule for the volume.
The active rule is as follows:
Rule_Index: 1
Client_Match: 10.50.0.0/16
Protocols: nfs4
Read_Only_Access: sys
Read_Write_Access: -
Superuser_Access: none
The Kubernetes pod that failed has an IP address of '10.60.5.10'.
What is the cause of the "Permission Denied" error?

A. The export policy only allows access via the NFSv4 protocol.
B. The 'Superuser_Access' setting is too restrictive.
C. The export policy does not grant read-write access, which is required by the anonymization job.
D. The pod's IP address ('10.60.5.10') is not within the allowed client match range ('10.50.0.0/16').

Answer: D

NEW QUESTION # 22
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