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Network Appliance NS0-901 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| AI Lifecycle and Deployment | - End-to-end AI lifecycle
|
| AI Infrastructure and NetApp Solutions | - Converged workloads
|
| Industry Use Cases | - AI applications across industries
|
| AI and Machine Learning Fundamentals | - AI, ML, DL concepts
|
Network Appliance NetApp Certified AI Expert Sample Questions:
The HPC cluster generates simulation data at an extremely high rate, requiring a storage system that can handle massively parallel writes from hundreds of compute nodes simultaneously. Which storage system and file protocol combination is the most appropriate choice for the HPC cluster's high-performance scratch space?
- A. A NetApp E-Series system serving a BeeGFS parallel file system.
- B. A NetApp StorageGRID system accessed via the S3 protocol.
- C. A Cloud Volumes ONTAP instance with a standard file system.
- D. A NetApp ASA system serving a single, large NFS volume.
Given the firm's requirements for using a private, constantly updated knowledge base and the strict mandate for data traceability, which AI architecture is the most appropriate foundation for the "Advisor Assistant" chatbot?
- A. A standalone LLM deployed in an air-gapped environment with no access to the document repository.
- B. A Retrieval-Augmented Generation (RAG) architecture that retrieves relevant context from a local vector database to enrich prompts sent to the LLM.
- C. A predictive AI model trained to forecast market trends.
- D. A fine-tuning architecture where a base LLM is continuously retrained on the entire document repository.
An architect is designing the storage and network infrastructure for a new, large-scale AI cluster dedicated to training foundational models. The primary design goal is to achieve the highest possible data throughput and the lowest latency to ensure multi-million dollar GPU resources are never idle. Which two technologies are essential to include in the design to achieve this goal?
(Choose 2.)
- A. NetApp StorageGRID as the primary storage for the training datasets.
- B. A 10GbE Ethernet network for all data traffic.
- C. An InfiniBand or RoCE-capable Ethernet network fabric.
- D. A tiered storage architecture using NetApp FabricPool.
- E. GPUDirect Storage support on the storage system.
A training job on one of the NVIDIA DGX servers is running slowly. A performance engineer runs the 'dstat' command on the server and captures the following output during the job execution.
-total-cpu-usage- -dsk/total- -net/total- paging-- system-- usr sys idl wai hiq siq| read writ| recv send| in out | int csw 15 5 70 10 0 0| 1.2G 15.0M| 1.2G 12.0M| 0 0 | 15k 35k
14 6 69 11 0 0| 1.2G 14.3M| 1.2G 11.8M| 0 0 | 14k 33k
16 5 68 11 0 0| 1.2G 16.1M| 1.2G 13.1M| 0 0 | 16k 36k
The server is connected to the NetApp ASA via a 100GbE (12.5 GB/s) network.
What is the most likely performance bottleneck based on this data?
- A. The server's network interface is saturated.
- B. The server's CPU is overloaded.
- C. The application is experiencing excessive memory paging.
- D. The storage system (ASA) is unable to provide sufficient read throughput.
A development team is building a generative AI application that must answer questions based on a constantly changing internal knowledge base of company documents. They want to provide the model with up-to-date information without altering its core weights and capabilities.
Which approach is most suitable for this requirement?
- A. Retrieval-Augmented Generation (RAG), which fetches relevant information from the documents at query time to inform the model's response.
- B. Predictive AI, which analyzes the documents to forecast future questions.
- C. Fine-tuning, which retrains the model's last few layers on the new documents.
- D. Unsupervised learning, which clusters the documents into topics for the model to browse.
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