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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Infrastructure and NetApp Solutions	- Converged workloads 1. AI, HPC, and analytics convergence 2. Shared infrastructure design considerations - AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads
Topic 2: AI and Machine Learning Fundamentals	- Algorithm types 1. Supervised learning 2. Unsupervised learning 3. Reinforcement learning - AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows
Topic 3: Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Autonomous systems and agents 3. Digital twins
Topic 4: AI Lifecycle and Deployment	- Operational challenges 1. Data governance and management 2. Scalability and performance optimization - End-to-end AI lifecycle 1. Model deployment and monitoring 2. Model training and validation 3. Data ingestion and preparation

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q42-Q47):

NEW QUESTION # 42
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. GPUDirect Storage support on the storage system.
B. A tiered storage architecture using NetApp FabricPool.
C. NetApp StorageGRID as the primary storage for the training datasets.
D. A 10GbE Ethernet network for all data traffic.
E. An InfiniBand or RoCE-capable Ethernet network fabric.

Answer: A,E

NEW QUESTION # 43
A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?

A. Both the data scientist and the DevOps engineer should use Jupyter Notebooks.
B. Both the data scientist and the DevOps engineer should use automated production pipelines.
C. The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
D. The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.

Answer: C

NEW QUESTION # 44
The firm's data science team needs to run a high-priority, interactive model analysis job that requires immediate access to two GPUs. However, all GPUs in the cluster are currently allocated to long-running, lower-priority batch training jobs.
The MLOps platform, Run:AI, shows the following queue status:
JOB_ID | PROJECT | STATUS | PRIORITY | GPU_ALLOCATED
||--|-|
batch_job_1 | team_a | Running | Low | 2
batch_job_2 | team_a | Running | Low | 2
batch_job_3 | team_b | Running | Low | 4
interactive_1| team_c | Pending | High | 2 (requested)
How does the Run:AI platform address this resource contention to allow the high-priority job to run?

A. It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
B. It automatically terminates all low-priority jobs to free up the entire cluster.
C. It sends an email notification to the administrator to manually reallocate the GPUs.
D. It automatically pauses one of the low-priority jobs, saves its state, and allocates its GPUs to the high- priority job, placing the paused job back in the queue.

Answer: D

NEW QUESTION # 45
An AI team is embarking on a project to train a new, large-scale computer vision model from scratch. The lead architect emphasizes that the success of the project depends on four fundamental inputs that must be available and managed throughout the training process. Which of the following are the four essential requirements for model generation?

A. A project manager, a data scientist, a software engineer, and a budget.
B. A pre-trained model, a validation set, an inference engine, and a cloud provider.
C. A data lake, a data warehouse, a data pipeline, and a data mart.
D. Data, code, compute, and time.

Answer: D

NEW QUESTION # 46
A pharmaceutical company is creating a "digital twin" of its manufacturing process. This involves running complex simulations (an HPC workload) that generate massive datasets.
The company wants to use this data immediately for two other purposes:
1. Analytics: Business analysts need to run complex queries on the simulation output using tools like Spark.
2. AI Training: Data scientists need to use the same output as a training set for a predictive maintenance model.
The company wants to avoid creating separate data silos for each workload.
Which two NetApp technologies are best suited for building a unified data lake that can efficiently serve all three workloads (HPC, Analytics, AI)? (Choose 2.)

A. NetApp Keystone to provide a flexible, pay-as-you-go consumption model for the infrastructure.
B. NetApp SnapCenter to create application-consistent backups of the data.
C. NetApp Autonomous Ransomware Protection to secure the data from modification.
D. NetApp ONTAP with FlexGroup volumes to provide high-throughput, parallel NFS access for the HPC and AI training workloads.
E. NetApp StorageGRID to provide a scalable, S3-native object store that integrates directly with modern analytics platforms like Spark.

Answer: D,E

NEW QUESTION # 47
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