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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cloud and Hybrid Cloud AI Deployment | 18% | - Data mobility and consistency across environments - NetApp cloud data services for AI - Cloud-native AI solutions and integration - Hybrid and multi-cloud AI architectures |
| Topic 2: 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 |
| Topic 3: AI Overview | 15% | - AI industry use cases and applications - AI, machine learning, and deep learning concepts - Convergence of AI, high-performance computing, and analytics - AI deployment models: on-premises, cloud, edge - Algorithm types: supervised, unsupervised, reinforcement learning |
| Topic 4: AI Lifecycle | 27% | - AI governance, ethics, and compliance - Data preparation and management for AI - AI lifecycle stages: design, training, deployment, monitoring - Model training, inference, and optimization - Predictive vs generative AI |
| Topic 5: 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 # 93
An architect is designing the storage and network infrastructure for a new, large-scale AI cluster dedicated to training foundational models. The primary design goal is to achieve the highest possible data throughput and the lowest latency to ensure multi-million dollar GPU resources are never idle. Which two technologies are essential to include in the design to achieve this goal?
(Choose 2.)
Answer: A,B
NEW QUESTION # 94
Advisors report that some queries to the chatbot are unacceptably slow, taking several seconds to respond. The MLOps team isolates the issue to the RAG retrieval step. Performance monitoring of the NetApp AFF A-Series hosting the vector database shows the following metrics during periods of high query load.
avg_read_latency: 3500 microseconds (3.5 ms)
avg_write_latency: 400 microseconds (0.4 ms)
iops_total: 15,000
cpu_utilization_storage_node: 15%
workload_profile: 95% small, random reads
Given these metrics, what is the most likely performance bottleneck?
Answer: D
NEW QUESTION # 95
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: B,C
NEW QUESTION # 96
The firm decides to expand the "Advisor Assistant" project to a new team in a different department. This team needs its own isolated environment. An MLOps engineer attempts to submit a new GPU- intensive job for the new team, but it remains pending. The engineer checks the Run:AI scheduler logs and finds the following entry:
time="2025-07-11T16:45:00Z" level=info msg="Job ds-new-team-job1 cannot be scheduled.
Project 'new-team-project' has exceeded its GPU quota. Quota: 0, Requested: 1, Used: 0." What is the root cause of the scheduling failure?
Answer: A
NEW QUESTION # 97
Which of the following techniques are used to maximize GPU utilization in AI workloads? (Choose two)
Answer: B,C
NEW QUESTION # 98
......
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