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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q12-Q17):

NEW QUESTION # 12
To comply with the security mandate, the architect must design a process to prevent client PII from the portfolio database from ever being included in the context sent to the LLM. Which two actions are required to build a robust and automated solution for this? (Choose 2.)

A. Rely on a disclaimer in the chatbot UI telling advisors not to enter queries related to specific clients.
B. Fine-tune the LLM on a sanitized dataset to teach it to ignore PII.
C. Use BlueXP classification to perform an initial scan of the portfolio database to identify and tag all columns containing PII.
D. Move the entire client portfolio database to an encrypted, air-gapped system.
E. Implement a data preparation step in the RAG pipeline that programmatically masks or excludes any data fields tagged as PII before the context is constructed.

Answer: C,E

NEW QUESTION # 13
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. The data scientist should use a Jupyter Notebook, and the DevOps engineer should use an automated production pipeline (e.g., Kubeflow Pipelines, Airflow).
B. The data scientist should use a production pipeline, and the DevOps engineer should use a Jupyter Notebook.
C. Both the data scientist and the DevOps engineer should use automated production pipelines.
D. Both the data scientist and the DevOps engineer should use Jupyter Notebooks.

Answer: A

NEW QUESTION # 14
An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

A. They are specialized storage systems designed to hold large datasets for training.
B. They are integrated development environments (IDEs) used exclusively for writing Python code.
C. They are networking protocols designed to accelerate data transfer between GPUs.
D. They are orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring.

Answer: D

NEW QUESTION # 15
Which AI technology is used to generate new, never-before-seen content such as images or text?

A. Reinforcement AI
B. Generative AI
C. Supervised AI
D. Predictive AI

Answer: B

NEW QUESTION # 16
An AI architect is reviewing the design for a new data lake. The primary requirement is to store petabytes of unstructured data (images, video, sensor logs) in a highly durable, scalable, and cost- effective manner. The data will be accessed via S3 API by various data processing and analytics applications.
The initial design proposes using a traditional Network Attached Storage (NAS) filer with a large number of disks. The architect reviews the proposal:
Proposed_System: Traditional NAS Filer
Protocol: NFSv4
Scalability_Model: Scale-up
Metadata_Handling: Centralized in filer head
Cost_per_GB: Moderate
Why is this proposed system a poor choice for a petabyte-scale data lake?

A. A NAS filer cannot be deployed on-premises.
B. NFS is incapable of storing image or video files.
C. A traditional scale-up NAS system will face scalability and cost-effectiveness challenges at the petabyte scale compared to an object storage system.
D. The S3 API cannot be used to access data stored on an NFS file system.

Answer: C

NEW QUESTION # 17
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