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| Section | Weight | Objectives |
|---|---|---|
| AI Overview | 15% | - Algorithm types: supervised, unsupervised, reinforcement learning - AI industry use cases and applications - AI, machine learning, and deep learning concepts - AI deployment models: on-premises, cloud, edge - Convergence of AI, high-performance computing, and analytics |
| AI Lifecycle | 27% | - Data preparation and management for AI - AI lifecycle stages: design, training, deployment, monitoring - Model training, inference, and optimization - Predictive vs generative AI - AI governance, ethics, and compliance |
| 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 - Data mobility and consistency across environments - Hybrid and multi-cloud AI architectures - NetApp cloud data services for AI |
| NetApp AI Solutions and Architecture | 25% | - Scalability and performance optimization for AI - ONTAP integration with AI frameworks - Data management and data pipeline design - Storage architectures for AI workloads - NetApp AI-ready infrastructure components |
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NEW QUESTION # 33
Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)
Answer: B,E
NEW QUESTION # 34
Which of the following are typical requirements for inferencing in AI models? (Choose two)
Answer: A,D
NEW QUESTION # 35
In the data pipeline, which NetApp technology is used to create an efficient, block-level, incremental replication of the anonymized datasets from the on-premises ASA to the Cloud Volumes ONTAP instance in Azure?
Answer: D
NEW QUESTION # 36
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 # 37
An architect is designing a scalable, automated MLOps platform using Kubeflow on a Kubernetes cluster. The platform must support the entire AI lifecycle for multiple teams, with different storage requirements at each stage.
The key requirements are:
- Data Ingestion: A pipeline step needs a shared, read-write volume accessible by multiple pods to stage raw data.
- Experimentation: Data scientists need individual, isolated volumes for their Jupyter notebooks.
- Training: Distributed training jobs require a high-performance, parallel-access filesystem for reading training data.
- Automation: All storage must be provisioned automatically via Kubeflow pipeline definitions without manual intervention.
Which combination of technologies and configurations would create the most effective solution?
Answer: C,E
NEW QUESTION # 38
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