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| Section | Weight | Objectives |
|---|
| Topic 1: 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
|
| Topic 2: 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
|
| Topic 3: AI Hardware Architectures | 18% | - NetApp Architectures
- 1. BasePod
- 2. OVX architectures
- 3. SuperPOD
- Infrastructure Topologies
- 1. Data aggregation and compute topologies
- Networking and Storage
- 1. Storage architectures for AI
- 2. Network protocols for AI workloads
|
| Topic 4: AI Overview | 15% | - AI Deployment Models
- 1. Edge
- 2. On-premises
- 3. Benefits and risks of each model
- 4. Cloud
- Algorithm Types
- 1. Reinforcement learning
- 2. Supervised learning
- 3. Unsupervised learning
- Training vs. Inferencing vs. Predictions
- 1. Distinguish between training and inference workloads
- AI Convergence with HPC and Analytics
- 1. Leveraging shared infrastructure for AI, HPC, and analytics
- AI Industry Applications
- 1. Digital twins
- 2. Healthcare applications
- 3. Agents
- Machine Learning Fundamentals
- 1. Describe machine learning benefits
- 2. Understand the relationship between AI, machine learning, and deep learning
|
| Topic 5: AI Lifecycle | 27% | - Data Preparation
- 1. Data aggregation and cleansing
- 2. XCP and CopySync
- 3. NetApp BlueXP Classification
- Generative AI Concepts
- 1. Fine-tuning
- 2. Retrieval Augmented Generation (RAG)
- 3. Hallucinations
- Predictive AI vs. Generative AI
- 1. Large Language Models (LLMs)
- 2. Distinction between predictive and generative AI
- 3. Impact of generative content (text, images, video, decision-making)
- Model Development
- 1. Inferencing
- 2. Model building
- 3. Fine-tuning workflows
|
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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q18-Q23):
NEW QUESTION # 18
Which storage protocols are commonly used for handling large-scale AI data? (Choose two)
- A. File-based systems
- B. Parallel file systems
- C. POSIX-based file systems
- D. Object-based storage
Answer: B,D
NEW QUESTION # 19
Which of the following platforms can be used to manage containerized AI workloads on Kubernetes? (Choose two)
- A. TensorFlow Extended
- B. KubeFlow
- C. Google VertexAI
- D. RunAI
Answer: B,D
NEW QUESTION # 20
In the data pipeline, which NetApp technology is used to create an efficient, block-level, incremental replication of the anonymized datasets from the on-premises ASA to the Cloud Volumes ONTAP instance in Azure?
- A. NetApp XCP
- B. NetApp SnapMirror
- C. NetApp BlueXP copy and sync
- D. NetApp FabricPool
Answer: B
NEW QUESTION # 21
The firm wants to extend the "Advisor Assistant" to include a new batch processing feature. Every night, the system must analyze every client portfolio against a set of 50 different risk models and generate a compliance report. This is a highly parallel, read-intensive workload. The architect must design a data workflow that is efficient and does not impact the production chatbot environment. Which sequence of actions and technologies provides the most effective solution?
- A. Create a full physical copy of the client portfolio database to a separate volume, mount it to the compute nodes, and run the analysis.
- B. Create a NetApp Snapshot of the portfolio database volume, create a FlexClone from that snapshot, mount the FlexClone to the analysis pods, and run the batch job.
- C. Use NetApp SnapMirror to replicate the portfolio database volume to the DR site, and run the analysis jobs there.
- D. Run the analysis job directly against the production portfolio database during off-peak hours.
Answer: B
NEW QUESTION # 22
An AI infrastructure engineer is troubleshooting a poorly performing distributed training job. The job is running across multiple nodes, each equipped with powerful GPUs. The engineer observes that overall GPU utilization is unexpectedly low. System-level monitoring on the compute nodes provides the following metrics during a training run.
avg_gpu_utilization: 25%
avg_cpu_iowait_percent: 65%
avg_network_bandwidth_util: 95% (on a 10GbE network)
storage_array_latency: <1ms
Given these metrics, what is the most likely bottleneck causing the low GPU utilization?
- A. The network connecting the compute nodes and storage is saturated and has become the primary bottleneck.
- B. The CPU is underpowered and cannot preprocess the data fast enough for the GPUs.
- C. The storage array is too slow and cannot serve data quickly enough.
- D. The GPUs are faulty and cannot process data at their rated speed.
Answer: A
NEW QUESTION # 23
......
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