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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cloud and Hybrid Cloud AI Deployment | 18% | - NetApp cloud data services for AI - Data mobility and consistency across environments - Cloud-native AI solutions and integration - Hybrid and multi-cloud AI architectures |
| Topic 2: AI Overview | 15% | - AI deployment models: on-premises, cloud, edge - Algorithm types: supervised, unsupervised, reinforcement learning - AI, machine learning, and deep learning concepts - Convergence of AI, high-performance computing, and analytics - AI industry use cases and applications |
| Topic 3: NetApp AI Solutions and Architecture | 25% | - NetApp AI-ready infrastructure components - Data management and data pipeline design - Storage architectures for AI workloads - ONTAP integration with AI frameworks - Scalability and performance optimization for AI |
| Topic 4: AI Lifecycle | 27% | - AI governance, ethics, and compliance - Data preparation and management for AI - Model training, inference, and optimization - Predictive vs generative AI - AI lifecycle stages: design, training, deployment, monitoring |
| Topic 5: Security, Reliability, and Operations | 15% | - High availability and data protection - Monitoring, logging, and troubleshooting AI environments - Cost management and efficiency - Data security and access control for AI |
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NEW QUESTION # 29
A healthcare organization plans to use a large dataset of patient records to train a predictive model. Before training, they must identify and segregate all records containing Personally Identifiable Information (PII) to comply with privacy regulations. The data resides on an on- premises NetApp ONTAP cluster. The organization needs an automated tool to scan the data in- place and tag files containing PII without moving the data.
The project requirements are as follows:
Task: Identify PII in a large dataset.
Data_Location: On-premises ONTAP cluster.
Constraint: Data must not be moved from its source location for scanning.
Output: Tagged files containing PII.
Which NetApp tool is designed for this specific task?
Answer: B
NEW QUESTION # 30
A university is building a shared AI research platform. They have two primary requirements:
1. Performance: A "hot" research area for active model training and development that requires the absolute lowest latency and highest throughput to support multiple, simultaneous GPU- intensive jobs.
The data in this area is around 50 TB.
2. Capacity & Cost: A "cold" data lake to store over 5 PB of raw, unstructured experimental data that is infrequently accessed but must be retained for compliance and future use. This tier must be as costeffective as possible.
Which combination of NetApp hardware and technologies should an architect select to build a complete, optimized, and cost-effective solution? (Select all that apply.)
Answer: A,C,D,F
NEW QUESTION # 31
The data scientists report that their Kubernetes-based data preparation jobs are failing. The pods are stuck in a 'Pending' state.
An MLOps engineer runs 'kubectl describe pvc data-prep-pvc-01' and sees the following event:
Type Reason Age From Message
- - - -
Warning ProvisioningFailed 2m15s trident-orchestrator-7b... failed to provision volume with StorageClass "bronze-tier": backend unavailable: no healthy backend with satisfying attributes for storage class "bronze-tier" The engineer checks the Trident backend configurations and finds no backend associated with the "bronze-tier" StorageClass.
What is the root cause of the failure?
Answer: D
NEW QUESTION # 32
A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)
Answer: A,B
NEW QUESTION # 33
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: A
NEW QUESTION # 34
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