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| Section | Weight | Objectives |
|---|
| AI Hardware Architectures | 18% | - Infrastructure Topologies
- 1. Data aggregation and compute topologies
- Networking and Storage
- 1. Network protocols for AI workloads
- 2. Storage architectures for AI
- NetApp Architectures
- 1. OVX architectures
- 2. BasePod
- 3. SuperPOD
|
| AI Overview | 15% | - Algorithm Types
- 1. Supervised learning
- 2. Unsupervised learning
- 3. Reinforcement learning
- AI Deployment Models
- 1. Edge
- 2. Benefits and risks of each model
- 3. Cloud
- 4. On-premises
- AI Convergence with HPC and Analytics
- 1. Leveraging shared infrastructure for AI, HPC, and analytics
- AI Industry Applications
- 1. Agents
- 2. Digital twins
- 3. Healthcare applications
- Training vs. Inferencing vs. Predictions
- 1. Distinguish between training and inference workloads
- Machine Learning Fundamentals
- 1. Understand the relationship between AI, machine learning, and deep learning
- 2. Describe machine learning benefits
|
| AI Common Challenges | 22% | - Traceability and Optimization
- 1. Maximizing performance in demanding AI workloads
- 2. Optimizing data access and movement
- 3. Ensuring traceability for code, data, and models
- Resource Management
- 1. Sizing storage and compute resources effectively
- 2. Controlling costs and securing storage
|
| AI Software Architectures | 18% | - Development Tools
- 1. NetApp DataOps Toolkit
- 2. Jupyter notebooks vs. pipelines
- 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
|
| AI Lifecycle | 27% | - Predictive AI vs. Generative AI
- 1. Impact of generative content (text, images, video, decision-making)
- 2. Large Language Models (LLMs)
- 3. Distinction between predictive and generative AI
- Model Development
- 1. Inferencing
- 2. Fine-tuning workflows
- 3. Model building
- Generative AI Concepts
- 1. Retrieval Augmented Generation (RAG)
- 2. Hallucinations
- 3. Fine-tuning
- Data Preparation
- 1. Data aggregation and cleansing
- 2. NetApp BlueXP Classification
- 3. XCP and CopySync
|
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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q62-Q67):
NEW QUESTION # 62
A network administrator, attempting to harden the security of the data center, modifies a firewall access control list (ACL). Immediately afterward, the "Advisor Assistant" application pods can no longer mount their required NFS volumes from the AFF A-Series. The MLOps team confirms the pods are stuck in a 'ContainerCreating' state with a 'MountVolume.SetUp failed... connection timed out' error. The administrator provides the new, active firewall rule:
RULE | ACTION | PROTOCOL | SOURCE_IP_RANGE | DEST_IP_RANGE | DEST_PORT --|--|-
|--||--51 | ALLOW | TCP | 10.20.5.0/24 | 10.20.10.0/24 | 2049
What is the most likely reason for the mount failures?
- A. The firewall rule is blocking the NFS lock manager (NLM) protocol, which is required for file locking.
- B. The firewall rule is blocking the NFSv4 protocol, which requires TCP port 2050.
- C. The firewall rule is blocking the Portmapper/RPCbind service (TCP/UDP port 111), which is necessary for the initial NFS mount negotiation.
- D. The 'SOURCE_IP_RANGE' is incorrect and does not include the Kubernetes pod IP addresses.
Answer: C
NEW QUESTION # 63
Which AI technology is used to generate new, never-before-seen content such as images or text?
- A. Predictive AI
- B. Generative AI
- C. Supervised AI
- D. Reinforcement AI
Answer: B
NEW QUESTION # 64
A junior administrator is attempting to delete an old, unused Snapshot copy from a production volume to reclaim space but receives an error. The administrator is certain they are using the correct command and have the appropriate 'vsadmin' role.
The command and error are shown below:
cluster-1::> volume snapshot delete -vserver svm_prod -volume app_data -snapshot nightly.2025- 06-15_0015 Error: command failed: This operation is not permitted. Deletion of Snapshot copy "nightly.2025-
06-15_0015" on volume "app_data" in Vserver "svm_prod" requires approval.
Use the "security multi-admin-verify approval show" command to view pending approvals.
What is the most likely reason the administrator cannot delete the Snapshot?
- A. The volume is protected by SnapLock, and the Snapshot has not reached its expiry date.
- B. The Snapshot is locked by a SnapMirror relationship.
- C. The 'vsadmin' role does not have permission to delete Snapshots.
- D. Multi-Admin Verification (MAV) is enabled, and the destructive operation requires approval from another designated administrator.
Answer: D
NEW QUESTION # 65
The AI training jobs on the AIPod are performing below expectations. The NVIDIA DGX servers' GPUs show low utilization. A performance analysis reveals that the bottleneck is not the storage system itself, but the network path between the storage and the compute nodes.
The current network configuration is as follows:
Network_Fabric: 100GbE Standard Ethernet
Protocol: NFS over TCP/IP
Data_Path: Storage -> Host CPU -> GPU Memory
Which network architecture enhancement would provide the most significant performance improvement by reducing latency and CPU overhead?
- A. Add more 100GbE network ports to the storage controllers.
- B. Upgrade the network switches to a model with a larger packet buffer.
- C. Implement RDMA over Converged Ethernet (RoCE) and configure GPUDirect Storage.
- D. Isolate the storage traffic on a separate VLAN from the management traffic.
Answer: C
NEW QUESTION # 66
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.)
- A. Replace all existing GPUs with the next-generation model to increase the performance of each node.
- B. Manually deploy all new application replicas to a single, new, very large "super-node".
- C. Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.
- D. Horizontally scale the cluster by adding new GPU-equipped nodes.
- E. Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
Answer: C,D
NEW QUESTION # 67
......
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