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| Section | Weight | Objectives |
|---|---|---|
| Security, Reliability, and Operations | 15% | - Monitoring, logging, and troubleshooting AI environments - High availability and data protection - Cost management and efficiency - Data security and access control for AI |
| Cloud and Hybrid Cloud AI Deployment | 18% | - Data mobility and consistency across environments - Hybrid and multi-cloud AI architectures - NetApp cloud data services for AI - Cloud-native AI solutions and integration |
| AI Lifecycle | 27% | - Model training, inference, and optimization - AI governance, ethics, and compliance - AI lifecycle stages: design, training, deployment, monitoring - Predictive vs generative AI - Data preparation and management for AI |
| AI Overview | 15% | - Convergence of AI, high-performance computing, and analytics - Algorithm types: supervised, unsupervised, reinforcement learning - AI industry use cases and applications - AI deployment models: on-premises, cloud, edge - AI, machine learning, and deep learning concepts |
| NetApp AI Solutions and Architecture | 25% | - NetApp AI-ready infrastructure components - Data management and data pipeline design - Storage architectures for AI workloads - Scalability and performance optimization for AI - ONTAP integration with AI frameworks |
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NEW QUESTION # 76
An MLOps team is using the BlueXP Workload Factory to automate the deployment of a Jupyter notebook environment for a new data scientist. The deployment job fails. The team reviews the job details in the BlueXP UI.
Job ID: 78a1c-2b3d-4e5f
Service: BlueXP Workload Factory
Action: Deploy JupyterHub on Kubernetes
Status: FAILED
Error Message: "Failed to create PersistentVolumeClaim 'jupyter-pvc-user01'.
User 'service-account- bluexp' does not have permission to 'create' resource
'persistentvolumeclaims' in cluster 'k8s-prod- cluster-01'."
What is the most direct way to resolve this issue?
Answer: D
NEW QUESTION # 77
What is the primary reason for implementing a hybrid cloud architecture in this medical imaging scenario?
Answer: B
NEW QUESTION # 78
An AI research team is experiencing slow model training times. Their performance monitoring indicates that the GPUs are frequently idle, waiting for data. They want to implement a single technology change to create a more direct data path between their storage and GPUs.
Their current setup is as follows:
Compute: Server with NVIDIA A100 GPUs
Storage: NetApp AFF A-Series (All-Flash)
Network: 100GbE Ethernet
Data_Path: Storage -> Host CPU/Memory -> GPU Memory
Which technology should the architect recommend to specifically address this data path inefficiency?
Answer: B
NEW QUESTION # 79
Which AI technology is used to generate new, never-before-seen content such as images or text?
Answer: A
NEW QUESTION # 80
An architect is designing a data pipeline for a predictive AI model that will forecast retail sales.
The pipeline must be robust, version-controlled, and efficient.
The proposed data flow is as follows:
1. Ingest: Raw sales data is copied daily from multiple point-of-sale (POS) systems to a central staging area on an on-premises ONTAP cluster.
2. Prepare: The raw data is messy. A data engineering team needs a clean, isolated, and writable copy of the latest daily data to perform cleansing and feature engineering tasks without impacting the original raw data.
3. Train: Once prepared, the cleansed dataset is used to retrain the predictive model on a GPU cluster.
This step must be repeatable with the exact same dataset for compliance.
4. Deploy: The newly trained model is pushed to production inference servers.
Which combination of NetApp technologies best supports this entire predictive AI lifecycle?
(Select all
that apply.)
Answer: B,E,F
NEW QUESTION # 81
......
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