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| Section | Weight | Objectives |
|---|---|---|
| AI Lifecycle | 27% | - AI governance, ethics, and compliance - Model training, inference, and optimization - AI lifecycle stages: design, training, deployment, monitoring - Predictive vs generative AI - Data preparation and management for AI |
| NetApp AI Solutions and Architecture | 25% | - NetApp AI-ready infrastructure components - Data management and data pipeline design - Scalability and performance optimization for AI - Storage architectures for AI workloads - ONTAP integration with AI frameworks |
| Security, Reliability, and Operations | 15% | - Cost management and efficiency - Data security and access control for AI - High availability and data protection - Monitoring, logging, and troubleshooting AI environments |
| Cloud and Hybrid Cloud AI Deployment | 18% | - Cloud-native AI solutions and integration - Data mobility and consistency across environments - Hybrid and multi-cloud AI architectures - NetApp cloud data services for AI |
| AI Overview | 15% | - AI industry use cases and applications - Algorithm types: supervised, unsupervised, reinforcement learning - AI, machine learning, and deep learning concepts - AI deployment models: on-premises, cloud, edge - Convergence of AI, high-performance computing, and analytics |
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NEW QUESTION # 73
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?
Answer: A
NEW QUESTION # 74
A research lab uses a fleet of autonomous drones to collect high-resolution aerial imagery for agricultural analysis. The drones land at a remote edge location and offload their data. The AI models for image analysis are trained at a central data center. The team is using NetApp SnapMirror to replicate the data from the edge to the core. However, the data scientists are complaining that the datasets arriving at the data center are often incomplete or corrupted.
An administrator reviews the SnapMirror configuration and status via the BlueXP API:
{
"source": { "workingEnvironmentId": "OnPrem-Edge-Filer-1", "volumeName": "drone_data_raw" },
"destination": { "workingEnvironmentId": "Core-Datacenter-A800", "volumeName":
"drone_data_replicated" },
"mirrorState": "broken-off",
"relationshipStatus": "idle",
"unhealthyReason": "Transfer failed. Destination volume is out of space.", "lastTransferInfo": {
"transferError": "No space left on device"
}
}
What is the direct cause of the incomplete datasets at the data center?
Answer: D
NEW QUESTION # 75
The company's compliance officer mandates that all raw simulation data in the StorageGRID data lake must be protected from modification or deletion for a period of seven years. Which two StorageGRID features should be implemented to meet this strict, long-term data immutability requirement? (Choose 2.)
Answer: A,B
NEW QUESTION # 76
An AI architect is designing a storage solution for a new training cluster. The primary workload consists of training large language models, which involves sequential reads of massive datasets.
The key requirement is to maximize GPU utilization by providing the highest possible data throughput. Cost is a secondary concern to performance.
Which NetApp storage system is the most appropriate choice for this workload?
Answer: C
NEW QUESTION # 77
A data science team works primarily at a central data center but needs to run a short-term, burst- compute training job in the public cloud to take advantage of specialized GPUs that are not available on- premises. They need to efficiently and securely move a 20 TB dataset from their on- premises ONTAP cluster to a Cloud Volumes ONTAP instance for the duration of the job.
The data flow requirement is as follows:
Source: On-premises ONTAP cluster
Destination: Cloud Volumes ONTAP in AWS
Requirement: Efficient, secure, block-level data transfer.
Which NetApp technology is the most appropriate tool for this task?
Answer: B
NEW QUESTION # 78
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