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| Section | Weight | Objectives |
|---|---|---|
| AI Lifecycle | 27% | - AI lifecycle stages: design, training, deployment, monitoring - Model training, inference, and optimization - Predictive vs generative AI - AI governance, ethics, and compliance - Data preparation and management for AI |
| Cloud and Hybrid Cloud AI Deployment | 18% | - Hybrid and multi-cloud AI architectures - Data mobility and consistency across environments - Cloud-native AI solutions and integration - NetApp cloud data services for AI |
| Security, Reliability, and Operations | 15% | - Cost management and efficiency - Data security and access control for AI - Monitoring, logging, and troubleshooting AI environments - High availability and data protection |
| NetApp AI Solutions and Architecture | 25% | - NetApp AI-ready infrastructure components - Scalability and performance optimization for AI - ONTAP integration with AI frameworks - Data management and data pipeline design - Storage architectures for AI workloads |
| AI Overview | 15% | - AI industry use cases and applications - AI deployment models: on-premises, cloud, edge - Convergence of AI, high-performance computing, and analytics - Algorithm types: supervised, unsupervised, reinforcement learning - AI, machine learning, and deep learning concepts |
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NEW QUESTION # 99
A new team of external auditors requires read-only access to the raw financial product documentation stored in the StorageGRID data lake. The data is in a bucket named 'prod-docs'.
The security team must ensure the auditors can list and read objects but cannot write, delete, or modify any data. Which is the most appropriate method to grant this specific access?
Answer: A
NEW QUESTION # 100
An AI architect is planning the resource allocation for a new project. The primary task is to process millions of unlabeled customer reviews to identify naturally occurring groups or themes without any prior guidance.
The project requirements are summarized below:
Task: Discover hidden patterns in text data
Input_Data: 10 million unlabeled text reviews
Output: Clustered groups of related reviews
Supervision: None
Which type of machine learning algorithm is required for this task?
Answer: C
NEW QUESTION # 101
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.)
Answer: A,C,D,F
NEW QUESTION # 102
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: C
NEW QUESTION # 103
An AI infrastructure team is selecting a storage protocol for a new, high-performance computing (HPC) cluster that will be used for a weather modeling AI application. The application requires extremely high-throughput, parallel access from hundreds of compute nodes to a shared dataset.
The workload characteristics are as follows:
Access_Pattern: Massively parallel reads from many clients.
File_Type: Large, shared data files.
Latency_Sensitivity: High (for metadata operations).
Primary_Requirement: Maximum aggregate throughput.
Which storage protocol is best suited for this workload?
Answer: D
NEW QUESTION # 104
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