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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Digital twins 3. Autonomous systems and agents
Topic 2: AI and Machine Learning Fundamentals	- Algorithm types 1. Reinforcement learning 2. Supervised learning 3. Unsupervised learning - AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows
Topic 3: AI Lifecycle and Deployment	- Operational challenges 1. Scalability and performance optimization 2. Data governance and management - End-to-end AI lifecycle 1. Data ingestion and preparation 2. Model deployment and monitoring 3. Model training and validation
Topic 4: 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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q56-Q61):

NEW QUESTION # 56
A media company is building a new generative AI service. The project has two main components:
1. Data Lake & Fine-Tuning: A 300 TB repository of unstructured data (videos, images, text) stored as objects will be used to fine-tune a foundational model. This process requires a scalable, cost-effective storage solution that can integrate with cloud-native data processing tools like Apache Spark.
2. Inference & RAG: The fine-tuned model will be used in a customer-facing application that leverages Retrieval-Augmented Generation (RAG). To ensure low-latency responses, the RAG component requires extremely fast lookups from a 10 TB vector database.
The company needs a solution that optimizes both cost and performance for this entire lifecycle.
Which combination of NetApp technologies provides the most appropriate solution for this scenario?

A. Use a single, large NetApp ASA system for both the object data lake and the vector database.
B. Use NetApp StorageGRID for the data lake and a NetApp ASA system for the vector database.
C. Use NetApp Cloud Volumes ONTAP for the data lake and NetApp StorageGRID for the vector database.
D. Use NetApp E-Series for the data lake and a NetApp ASA system for the vector database.

Answer: B

NEW QUESTION # 57
Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)

A. Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
B. Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.
C. Replace all existing GPUs with the next-generation model to increase the performance of each node.
D. Manually deploy all new application replicas to a single, new, very large "super-node".
E. Horizontally scale the cluster by adding new GPU-equipped nodes.

Answer: B,E

NEW QUESTION # 58
Given the firm's requirements for using a private, constantly updated knowledge base and the strict mandate for data traceability, which AI architecture is the most appropriate foundation for the "Advisor Assistant" chatbot?

A. A Retrieval-Augmented Generation (RAG) architecture that retrieves relevant context from a local vector database to enrich prompts sent to the LLM.
B. A predictive AI model trained to forecast market trends.
C. A standalone LLM deployed in an air-gapped environment with no access to the document repository.
D. A fine-tuning architecture where a base LLM is continuously retrained on the entire document repository.

Answer: A

NEW QUESTION # 59
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. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
B. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
C. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
D. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
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,C,D,F

NEW QUESTION # 60
A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)

A. NetApp XCP
B. NetApp FabricPool
C. NetApp SnapMirror
D. NetApp FlexCache
E. NetApp StorageGRID

Answer: B,E

NEW QUESTION # 61
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