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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q11-Q16):

NEW QUESTION # 11
A financial services company is required by regulators to be able to trace any version of their deployed fraud detection model back to the exact dataset and source code commit used to train it.
The current MLOps workflow is as follows:
Code_Repository: Git (commit hash: a1b2c3d4)
Dataset_Location: /vol/prod_data/fraud_dataset_v3
Storage_System: NetApp ONTAP 9
Model_Output: /vol/models/fraud_model_v3.2
Which NetApp technology should be used to create an immutable, point-in-time, and space- efficient copy of the dataset that can be linked to the specific code commit and model version?

A. NetApp FlexClone
B. NetApp FabricPool
C. NetApp Snapshots
D. NetApp SnapMirror

Answer: C

NEW QUESTION # 12
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 only for on-premises deployments, while a data warehouse is only for cloud deployments.
B. 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.
C. A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.
D. A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.

Answer: B

A. Project A is a model building task and requires a significantly larger and more powerful compute and storage infrastructure than Project B.
B. Project A requires only a large amount of data, while Project B requires only a large amount of code.
C. Both projects are examples of fine-tuning and have similar infrastructure needs.
D. Project B is a fine-tuning task, which leverages an existing model and requires less data and compute resources than building a model from scratch.
E. Both projects should use NetApp StorageGRID as the primary storage for training to ensure low latency.

Answer: A,D

NEW QUESTION # 14
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 standard 10GbE network for all connectivity to reduce costs.
B. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
C. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
D. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
E. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
F. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.

Answer: C,D,E,F

NEW QUESTION # 15
An organization wants to provide its data science team with a secure, on-demand method for using a powerful generative AI model with their private, sensitive corporate data. The solution must ensure that the private data is never exposed to the public internet or the public LLM API endpoint.
The architect is designing a solution using BlueXP.
Which two components are essential for building this secure solution? (Choose 2.)

A. The BlueXP GenAI Toolkit, which acts as a proxy to intercept user prompts and enrich them with data from a local vector database.
B. A BlueXP Connector deployed in a public subnet with a public IP address to allow access to the LLM.
C. A private vector database hosted on an on-premises NetApp ONTAP system to store embeddings of the sensitive corporate data.
D. A policy in BlueXP classification to copy all sensitive data to a public cloud bucket for easier access.
E. A direct VPN connection from each data scientist's laptop to the public LLM provider.

Answer: A,C

NEW QUESTION # 16
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