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Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| AI Common Challenges | 22% | - Traceability and Optimization
|
| AI Lifecycle | 27% | - Generative AI Concepts
|
| AI Overview | 15% | - AI Industry Applications
|
| AI Software Architectures | 18% | - Development Tools
|
| AI Hardware Architectures | 18% | - Infrastructure Topologies
|
Network Appliance NetApp Certified AI Expert Sample Questions:
The company's finance department mandates a cost-control strategy for the petabyte-scale StorageGRID data lake. The analysis shows that 80% of the simulation data is not accessed after
90 days but must be retained for five years for regulatory compliance. Which StorageGRID feature should the architect use to automatically reduce the long-term storage costs for this inactive data?
- A. Use NetApp SnapMirror to replicate the cold data to a different, lower-cost StorageGRID cluster.
- B. Configure an Information Lifecycle Management (ILM) policy that automatically moves objects older than 90 days to a lower-cost cloud archive tier, such as Amazon S3 Glacier Deep Archive.
- C. Implement a FabricPool policy to tier the data to a NetApp ASA system.
- D. Enable deduplication and compression on all StorageGRID storage nodes.
Given the company's goal of combining physics-based simulations with AI-driven analytics on a shared data foundation, which industry trend does this project best represent?
- A. The convergence of AI, High-Performance Computing (HPC), and analytics on a unified data infrastructure.
- B. The replacement of all physical testing with digital simulations.
- C. The exclusive use of public cloud resources for all computational tasks.
- D. The separation of AI and HPC into dedicated, air-gapped environments.
A robotics company is developing a control system for an autonomous warehouse drone. The drone must learn to navigate complex environments to pick up packages. The development team has created a physics-based simulation where the drone can attempt the task millions of times.
The drone receives a positive reward for successfully retrieving a package and a negative penalty for collisions. Which type of machine learning algorithm is being used in this scenario?
- A. Supervised learning
- B. Generative learning
- C. Reinforcement learning
- D. Unsupervised learning
An enterprise is planning a generative AI solution to power its internal support chatbot. The architect must choose between a RAG-based approach and fine-tuning a base model. The project stakeholders have provided a list of prioritized requirements.
| Requirement | Priority | Details
|
| | -- | |
| Factual Accuracy | Critical | Must use the latest product documentation, updated daily.
| | Brand Voice & Persona | High | Must respond in the company's specific, formal tone.
| | Development Cost | High | Limited budget for GPU compute hours for model training.
|
| Data Traceability | Critical | Must be able to cite the exact source document for each answer.
|
Which two recommendations should the architect make to best satisfy these requirements?
(Choose 2.)
- A. Propose a hybrid approach where a base model is first lightly fine-tuned for persona, then used within a RAG system for factual grounding.
- B. Use RAG exclusively, as prompt engineering alone can fully replicate a specific brand voice and persona.
- C. Prioritize fine-tuning to embed the company's brand voice and persona into the model.
- D. Prioritize a RAG architecture to meet the critical requirements for factual accuracy and data traceability.
- E. Recommend training a new LLM from scratch to ensure both brand voice and factual accuracy are built-in.
An architect is designing a fully automated, end-to-end MLOps pipeline on Kubernetes for a computer vision use case. The pipeline must handle everything from data versioning to model deployment.
The required pipeline stages are:
1. Data Versioning: Create a new, immutable version of the master dataset for the pipeline run.
2. Data Preparation: Launch a pod to run a preprocessing script on the versioned data.
3. Model Training: Launch a distributed training job that reads the prepared data from a highperformance volume.
4. Model Deployment: Push the trained model to a production inference service.
Which combination of NetApp and Kubernetes technologies provides the most effective and automated solution for this entire pipeline?
- A. Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
- B. Use the NetApp DataOps Toolkit to create a Snapshot of the source data volume (for versioning), then create a FlexClone PVC from the snapshot for the preparation stage, and finally create a FlexGroup PVC for the training stage.
- C. Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.
- D. Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.
- E. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.
Lewis -
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