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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
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
AI Lifecycle and Deployment	- End-to-end AI lifecycle 1. Model training and validation 2. Model deployment and monitoring 3. Data ingestion and preparation - Operational challenges 1. Data governance and management 2. Scalability and performance optimization
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
Industry Use Cases	- AI applications across industries 1. Digital twins 2. Autonomous systems and agents 3. Healthcare AI

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q36-Q41):

NEW QUESTION # 36
An organization is developing a new AI-powered application. The initial phase involves feeding a curated 50 TB dataset of labeled images into a complex neural network, allowing the model to learn and adjust its internal parameters over millions of iterations. The second phase involves deploying this finalized model to a web service where it will process single, user-uploaded images and return a classification in real-time.
Which statement accurately describes these two phases?

A. Both Phase 1 and Phase 2 are examples of inferencing.
B. Phase 1 is training, and Phase 2 is inferencing.
C. Phase 1 is inferencing, and Phase 2 is training.
D. Both Phase 1 and Phase 2 are examples of training.

Answer: B

NEW QUESTION # 37
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 BlueXP copy and sync
B. NetApp FabricPool
C. NetApp SnapMirror
D. NetApp XCP

Answer: C

NEW QUESTION # 38
The pod running the vector database on the Kubernetes cluster fails to start. An MLOps engineer runs 'kubectl describe pod vector-db-pod-0' and sees the following event message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 30s default-scheduler 0/8 nodes are available: 8 node(s) did not match pod anti-affinity rules.
The pod's manifest contains the following 'affinity' definition:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app
operator: In
values:
- vector-db
topologyKey: "kubernetes.io/hostname"
What is the most likely reason the pod cannot be scheduled?

A. The 'topologyKey' is invalid; it should be 'failure-domain.beta.kubernetes.io/zone'.
B. The 'vector-db-pod-0' is requesting more GPU resources than are available on any node.
C. The PersistentVolumeClaim for the pod is in a 'Pending' state.
D. The pod's anti-affinity rule prevents it from being scheduled on any node that is already running another pod with the label 'app=vector-db'.

Answer: D

NEW QUESTION # 39
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 pre-trained model, a validation set, an inference engine, and a cloud provider.
B. A project manager, a data scientist, a software engineer, and a budget.
C. A data lake, a data warehouse, a data pipeline, and a data mart.
D. Data, code, compute, and time.

Answer: D

NEW QUESTION # 40
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. Use a standard 10GbE network for all connectivity to reduce costs.
D. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
E. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
F. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.

Answer: A,B,D,E

NEW QUESTION # 41
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