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| Section | Weight | Objectives |
|---|---|---|
| Security, Reliability, and Operations | 15% | - Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI - High availability and data protection - Cost management and efficiency |
| NetApp AI Solutions and Architecture | 25% | - NetApp AI-ready infrastructure components - Scalability and performance optimization for AI - Storage architectures for AI workloads - ONTAP integration with AI frameworks - Data management and data pipeline design |
| Cloud and Hybrid Cloud AI Deployment | 18% | - Hybrid and multi-cloud AI architectures - NetApp cloud data services for AI - Cloud-native AI solutions and integration - Data mobility and consistency across environments |
| AI Overview | 15% | - Convergence of AI, high-performance computing, and analytics - AI industry use cases and applications - AI, machine learning, and deep learning concepts - AI deployment models: on-premises, cloud, edge - Algorithm types: supervised, unsupervised, reinforcement learning |
| AI Lifecycle | 27% | - Predictive vs generative AI - Data preparation and management for AI - Model training, inference, and optimization - AI lifecycle stages: design, training, deployment, monitoring - AI governance, ethics, and compliance |
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NEW QUESTION # 86
A national research laboratory is investing in a turnkey AI infrastructure solution. Their primary goal is to eliminate the complexity and risk of designing and integrating the compute, network, and storage components themselves. The solution must be pre-validated by the vendors to deliver predictable, linear performance as they scale from one to multiple compute nodes. Which two options represent this type of pre-validated, converged infrastructure solution for AI? (Choose two)
Answer: B,E
NEW QUESTION # 87
Which of the following applications use AI in the healthcare industry? (Choose two)
Answer: C,D
NEW QUESTION # 88
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?
Answer: D
NEW QUESTION # 89
A healthcare organization plans to use a large dataset of patient records to train a predictive model. Before training, they must identify and segregate all records containing Personally Identifiable Information (PII) to comply with privacy regulations. The data resides on an on- premises NetApp ONTAP cluster. The organization needs an automated tool to scan the data in- place and tag files containing PII without moving the data.
The project requirements are as follows:
Task: Identify PII in a large dataset.
Data_Location: On-premises ONTAP cluster.
Constraint: Data must not be moved from its source location for scanning.
Output: Tagged files containing PII.
Which NetApp tool is designed for this specific task?
Answer: D
NEW QUESTION # 90
An organization is planning to deploy a large AI infrastructure but wants to avoid a large, upfront capital expenditure. They prefer an operational expenditure (OpEx) model where they pay for storage and compute resources as they are consumed. They also need the flexibility to scale resources up or down based on project demands.
Which NetApp consumption model is specifically designed to meet these financial and operational requirements?
Answer: A
NEW QUESTION # 91
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