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| Section | Weight | Objectives |
|---|
| AI Hardware Architectures | 18% | - Networking and Storage
- 1. Storage architectures for AI
- 2. Network protocols for AI workloads
- NetApp Architectures
- 1. BasePod
- 2. OVX architectures
- 3. SuperPOD
- Infrastructure Topologies
- 1. Data aggregation and compute topologies
|
| AI Lifecycle | 27% | - Predictive AI vs. Generative AI
- 1. Impact of generative content (text, images, video, decision-making)
- 2. Distinction between predictive and generative AI
- 3. Large Language Models (LLMs)
- Data Preparation
- 1. XCP and CopySync
- 2. Data aggregation and cleansing
- 3. NetApp BlueXP Classification
- Generative AI Concepts
- 1. Retrieval Augmented Generation (RAG)
- 2. Hallucinations
- 3. Fine-tuning
- Model Development
- 1. Fine-tuning workflows
- 2. Inferencing
- 3. Model building
|
| AI Overview | 15% | - AI Convergence with HPC and Analytics
- 1. Leveraging shared infrastructure for AI, HPC, and analytics
- AI Industry Applications
- 1. Healthcare applications
- 2. Digital twins
- 3. Agents
- Algorithm Types
- 1. Reinforcement learning
- 2. Unsupervised learning
- 3. Supervised learning
- AI Deployment Models
- 1. Edge
- 2. Benefits and risks of each model
- 3. On-premises
- 4. Cloud
- Machine Learning Fundamentals
- 1. Describe machine learning benefits
- 2. Understand the relationship between AI, machine learning, and deep learning
- Training vs. Inferencing vs. Predictions
- 1. Distinguish between training and inference workloads
|
| AI Common Challenges | 22% | - Traceability and Optimization
- 1. Maximizing performance in demanding AI workloads
- 2. Ensuring traceability for code, data, and models
- 3. Optimizing data access and movement
- Resource Management
- 1. Sizing storage and compute resources effectively
- 2. Controlling costs and securing storage
|
| AI Software Architectures | 18% | - MLOps and LLMOps Ecosystems
- 1. Understanding the software tools and platforms enabling AI at scale
- Scaling and Orchestration
- 1. Leveraging BlueXP software tools
- 2. Scaling AI workloads with Kubernetes
- Development Tools
- 1. NetApp DataOps Toolkit
- 2. Jupyter notebooks vs. pipelines
|
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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q66-Q71):
NEW QUESTION # 66
An AI infrastructure architect is tasked with designing a solution to address two critical challenges in a large, multi-petabyte AI environment:
1. Cost: A significant portion of the data on the high-performance all-flash storage is inactive but must remain online. The cost of storing this cold data on the performance tier is prohibitive.
2. Traceability: Data scientists need a simple, space-efficient way to version their datasets at key points in their workflow to ensure reproducibility.
The environment consists of NetApp AFF A-Series and NetApp StorageGRID systems.
Which combination of NetApp technologies should the architect implement to solve both challenges simultaneously? (Select all that apply.)
- A. Implement NetApp FabricPool to automatically tier cold data blocks from the AFF systems to StorageGRID.
- B. Train data scientists to use NetApp Snapshots to create point-in-time, read-only versions of their data volumes.
- C. Use NetApp XCP to periodically move cold data from the AFF systems to StorageGRID.
- D. Implement NetApp FlexClone to create full, writable copies of datasets for each experiment.
- E. Use BlueXP backup and recovery to create backups on StorageGRID, then delete the original volumes from the AFF systems.
- F. Use NetApp SnapMirror to replicate volumes from the AFF systems to StorageGRID for archival.
Answer: A,B
NEW QUESTION # 67
An organization has a core data center with a large AI training cluster and several remote edge locations for data ingest and local inference. The edge locations frequently need access to the latest models trained in the core data center, but WAN bandwidth is limited and can be unreliable.
Users at the edge are reporting slow model loading times.
An architect reviews the data access logs from an edge site:
Timestamp: 2025-07-11T15:30:00Z
Event: Model_Load_Request
Model_Path: nfs://core-filer.example.com/vol/models/latest_model.pkl
Source_IP: 192.168.100.15 (Edge Server)
Destination_IP: 10.1.1.50 (Core Filer)
Status: SUCCESS
Duration: 3600s (60 minutes)
What is the most likely cause of the slow model loading times at the edge?
- A. The model file is being transferred over a slow, high-latency WAN link for every load request.
- B. The core ONTAP filer is using slow, capacity-based disks.
- C. The NFS version used between the core and edge is outdated.
- D. The edge server does not have enough RAM to cache the model effectively.
Answer: A
NEW QUESTION # 68
A security administrator is reviewing the configuration of a production ONTAP cluster after a compliance audit. The audit requires that all volumes containing financial data must be protected against ransomware. The administrator runs a command to check the status of Autonomous Ransomware Protection (ARP) on a critical volume.
The command and its output are as follows:
cluster-1::> security ransomware anti-ransomware show -vserver svm_finance -volume finance_q1_data Vserver: svm_finance Volume: finance_q1_data Autonomous Ransomware Protection Status: disabled Learning Mode: normal Dry Run Mode: false Action on Detection: notify Based on this output, what is the primary security risk for the 'finance_q1_data' volume?
- A. The action on detection is set only to "notify," which will not block the attack.
- B. The learning mode is set to "normal," which may not detect zero-day attacks.
- C. The volume is not part of any Snapshot schedule.
- D. The volume is vulnerable to ransomware because Autonomous Ransomware Protection is not enabled.
Answer: D
NEW QUESTION # 69
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.)
- A. Enable GPUDirect Storage on the ASA system to provide the lowest latency data path to the GPUs.
- B. Use NetApp E-Series systems for both the hot tier and the cold data lake to simplify management.
- C. Implement NetApp FabricPool to automatically tier inactive data from the ASA system to the StorageGRID data lake.
- D. Use a NetApp All-SAN Array (ASA) system for the 50 TB high-performance "hot" research area.
- E. Use NetApp StorageGRID to build the 5 PB cost-effective data lake.
- F. Use a standard 10GbE network for all connectivity to reduce costs.
Answer: A,C,D,E
NEW QUESTION # 70
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?
- A. NetApp SnapMirror
- B. NetApp FlexClone
- C. NetApp FabricPool
- D. NetApp XCP
Answer: B
NEW QUESTION # 71
......
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