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| Section | Weight | Objectives |
|---|---|---|
| AI Overview | 15% | - AI, machine learning, and deep learning concepts - AI deployment models: on-premises, cloud, edge - Convergence of AI, high-performance computing, and analytics - AI industry use cases and applications - Algorithm types: supervised, unsupervised, reinforcement learning |
| Security, Reliability, and Operations | 15% | - High availability and data protection - Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI - Cost management and efficiency |
| Cloud and Hybrid Cloud AI Deployment | 18% | - Cloud-native AI solutions and integration - Hybrid and multi-cloud AI architectures - NetApp cloud data services for AI - Data mobility and consistency across environments |
| NetApp AI Solutions and Architecture | 25% | - Storage architectures for AI workloads - Data management and data pipeline design - NetApp AI-ready infrastructure components - Scalability and performance optimization for AI - ONTAP integration with AI frameworks |
| AI Lifecycle | 27% | - AI lifecycle stages: design, training, deployment, monitoring - Model training, inference, and optimization - AI governance, ethics, and compliance - Predictive vs generative AI - Data preparation and management for AI |
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NEW QUESTION # 14
Given the company's goal of combining physics-based simulations with AI-driven analytics on a shared data foundation, which industry trend does this project best represent?
Answer: A
NEW QUESTION # 15
An organization has a core data center with a large AI training cluster and several remote edge locations for data ingest and local inference. The edge locations frequently need access to the latest models trained in the core data center, but WAN bandwidth is limited and can be unreliable.
Users at the edge are reporting slow model loading times.
An architect reviews the data access logs from an edge site:
Timestamp: 2025-07-11T15:30:00Z
Event: Model_Load_Request
Model_Path: nfs://core-filer.example.com/vol/models/latest_model.pkl
Source_IP: 192.168.100.15 (Edge Server)
Destination_IP: 10.1.1.50 (Core Filer)
Status: SUCCESS
Duration: 3600s (60 minutes)
What is the most likely cause of the slow model loading times at the edge?
Answer: A
NEW QUESTION # 16
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: D
NEW QUESTION # 17
An organization recently suffered a ransomware attack that encrypted several volumes on their primary ONTAP storage system, including a critical volume containing curated training data. The security team needs to implement a solution that can proactively detect and block ransomware- like file I/O patterns and automatically create a secure Snapshot copy before any damage is done.
The current ONTAP configuration is as follows:
ONTAP_Version: 9.12.1
Security_Features: SnapLock (Compliance Mode) on archive volumes
Anti-Virus_Scan: Enabled (Vscan)
Ransomware_Detection: Not configured
Which ONTAP feature should be enabled to provide this proactive, automated protection?
Answer: C
NEW QUESTION # 18
An AI platform is suffering from poor performance during distributed training jobs. The training data resides on a single, large NFS volume. Monitoring shows that while the overall network throughput to the storage system is high, individual GPU nodes experience significant I/O wait times, and the single ONTAP volume is becoming a performance bottleneck. The goal is to re- architect the storage layout to maximize read parallelism and throughput for the training cluster.
Which two actions should the architect take to address this performance bottleneck? (Choose 2.)
Answer: A,D
NEW QUESTION # 19
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