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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
AI Lifecycle and Deployment	- Operational challenges 1. Scalability and performance optimization 2. Data governance and management - End-to-end AI lifecycle 1. Model training and validation 2. Data ingestion and preparation 3. Model deployment and monitoring
AI and Machine Learning Fundamentals	- AI, ML, DL concepts 1. Training, inference, and prediction workflows 2. Differences between AI, machine learning, and deep learning - Algorithm types 1. Reinforcement learning 2. Unsupervised learning 3. Supervised learning
AI Infrastructure and NetApp Solutions	- AI-ready data infrastructure 1. Data pipeline optimization for AI/ML workloads 2. High-performance storage for AI workloads - Converged workloads 1. Shared infrastructure design considerations 2. AI, HPC, and analytics convergence
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 (Q80-Q85):

NEW QUESTION # 80
An AI architect needs to design a complete, end-to-end data pipeline for a new generative AI application at a financial services firm. The application will allow internal analysts to query a massive, 500 TB archive of historical market data and reports to generate summaries. The firm has the following environment and requirements:
Data_Sources: A mix of on-premises ONTAP filers and StorageGRID S3 buckets.
Requirement_1: All queries must be answered using only the private data archive.
Requirement_2: All generated summaries must provide citations to the source reports.
Requirement_3: All data containing client PII must be identified and excluded from the LLM context.
Requirement_4: The solution must be cost-effective for the large, mostly-read data archive.
Which set of actions and technologies constitutes the most robust and compliant solution?
(Select all that apply.)

A. Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
B. Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
C. Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.
D. During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.
E. Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.
F. Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.

Answer: A,C,D,E

NEW QUESTION # 81
Which of the following are typical requirements for inferencing in AI models? (Choose two)

A. Cloud-based inference only
B. Pre-trained models
C. Loading the model into memory
D. Extensive training data

Answer: B,C

NEW QUESTION # 82
What is the primary difference between C-Series and A-Series storage for AI workloads?

A. C-Series is designed for high-performance workloads, while A-Series focuses on cost-efficiency for general storage.
B. A-Series provides GPU-based acceleration, while C-Series only supports CPU-based workloads.
C. C-Series supports both file and object-based storage, while A-Series is optimized for object storage.
D. A-Series is for smaller-scale AI workloads, while C-Series supports large-scale, high-performance AI applications.

Answer: A

NEW QUESTION # 83
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 predictive AI model trained to forecast market trends.
B. A fine-tuning architecture where a base LLM is continuously retrained on the entire document repository.
C. A standalone LLM deployed in an air-gapped environment with no access to the document repository.
D. A Retrieval-Augmented Generation (RAG) architecture that retrieves relevant context from a local vector database to enrich prompts sent to the LLM.

Answer: D

NEW QUESTION # 84
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 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.
B. It sends an email notification to the administrator to manually reallocate the GPUs.
C. It automatically terminates all low-priority jobs to free up the entire cluster.
D. It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.

Answer: A

NEW QUESTION # 85
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