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| Section | Weight | Objectives |
|---|---|---|
| Security, Reliability, and Operations | 15% | - Monitoring, logging, and troubleshooting AI environments - Data security and access control for AI - High availability and data protection - Cost management and efficiency |
| Cloud and Hybrid Cloud AI Deployment | 18% | - NetApp cloud data services for AI - Hybrid and multi-cloud AI architectures - Data mobility and consistency across environments - Cloud-native AI solutions and integration |
| AI Overview | 15% | - AI, machine learning, and deep learning concepts - Algorithm types: supervised, unsupervised, reinforcement learning - AI industry use cases and applications - Convergence of AI, high-performance computing, and analytics - AI deployment models: on-premises, cloud, edge |
| AI Lifecycle | 27% | - Predictive vs generative AI - AI lifecycle stages: design, training, deployment, monitoring - AI governance, ethics, and compliance - Data preparation and management for AI - Model training, inference, and optimization |
| NetApp AI Solutions and Architecture | 25% | - Scalability and performance optimization for AI - ONTAP integration with AI frameworks - NetApp AI-ready infrastructure components - Storage architectures for AI workloads - Data management and data pipeline design |
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NEW QUESTION # 61
An AI architect is designing a storage solution for a new training cluster. The primary workload consists of training large language models, which involves sequential reads of massive datasets.
The key requirement is to maximize GPU utilization by providing the highest possible data throughput. Cost is a secondary concern to performance.
Which NetApp storage system is the most appropriate choice for this workload?
Answer: D
NEW QUESTION # 62
A distributed training job running on the AIPod fails to start. The MLOps engineer inspects the events for one of the pending training pods and sees the following message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 5m12s default-scheduler 0/4 nodes are available: 4 node(s) had no available volume zone.
The PersistentVolumeClaim (PVC) for this pod specifies a StorageClass that uses the 'ontap-nas' Trident provisioner.
he Trident logs show no errors.
What is the most likely cause of this scheduling failure?
Answer: C
NEW QUESTION # 63
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 # 64
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: A,C,F
NEW QUESTION # 65
An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?
Answer: A
NEW QUESTION # 66
......
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