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| Section | Weight | Objectives |
|---|---|---|
| AI Lifecycle | 27% | - AI governance, ethics, and compliance - Data preparation and management for AI - Predictive vs generative AI - Model training, inference, and optimization - AI lifecycle stages: design, training, deployment, monitoring |
| Security, Reliability, and Operations | 15% | - Data security and access control for AI - High availability and data protection - Cost management and efficiency - Monitoring, logging, and troubleshooting AI environments |
| Cloud and Hybrid Cloud AI Deployment | 18% | - Cloud-native AI solutions and integration - Hybrid and multi-cloud AI architectures - Data mobility and consistency across environments - NetApp cloud data services for AI |
| AI Overview | 15% | - Convergence of AI, high-performance computing, and analytics - AI deployment models: on-premises, cloud, edge - AI, machine learning, and deep learning concepts - AI industry use cases and applications - Algorithm types: supervised, unsupervised, reinforcement learning |
| NetApp AI Solutions and Architecture | 25% | - Data management and data pipeline design - Scalability and performance optimization for AI - ONTAP integration with AI frameworks - Storage architectures for AI workloads - NetApp AI-ready infrastructure components |
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NEW QUESTION # 43
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 # 44
A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?
Answer: D
NEW QUESTION # 45
An online retail company's recommendation engine, which provides real-time product suggestions to users, is experiencing unacceptable latency. The inference application is running on a correctly-sized edge server, but user requests are taking over 500ms to process. An architect reviews the data access pattern and infrastructure diagram.
Application_Location: Edge Server (In-store)
Data_Source_Location: Core Data Center (On-premises ONTAP)
Data_Required_for_Inference: User profile data, product catalog vectors Network_Path: Edge -> WAN -> Core Data Center Observed_Latency: 550ms What is the most likely cause of the high inference latency?
Answer: B
NEW QUESTION # 46
The firm decides to implement a disaster recovery (DR) site for the "Advisor Assistant" application in a secondary data center. The Recovery Point Objective (RPO) is 15 minutes, and the Recovery Time Objective (RTO) is 4 hours. The design must protect both the document data lake and the vector database.
The primary site contains:
- Data Lake: NetApp StorageGRID
- Vector DB: NetApp AFF A-Series
Which combination of technologies and processes provides a complete and robust DR solution?
(Select all that apply.)
Answer: A,C,F
NEW QUESTION # 47
A data scientist is working on a new model and needs a flexible environment for interactive data exploration, code development, and quick visualizations. A DevOps engineer is responsible for deploying the finalized model into a production pipeline that must run automatically every night without manual intervention.
Which tools are best suited for each of these roles?
Answer: C
NEW QUESTION # 48
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