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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

Question 1

An AI team is embarking on a project to train a new, large-scale computer vision model from scratch. The lead architect emphasizes that the success of the project depends on four fundamental inputs that must be available and managed throughout the training process. Which of the following are the four essential requirements for model generation?

A. A project manager, a data scientist, a software engineer, and a budget.
B. A pre-trained model, a validation set, an inference engine, and a cloud provider.
C. Data, code, compute, and time.
D. A data lake, a data warehouse, a data pipeline, and a data mart.

Question 2

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 BlueXP copy and sync
B. NetApp FabricPool
C. NetApp SnapMirror
D. NetApp XCP

Question 3

An AI architect is designing a storage solution for a new training cluster. The primary workload consists of training large language models, which involves sequential reads of massive datasets.
The key requirement is to maximize GPU utilization by providing the highest possible data throughput. Cost is a secondary concern to performance.
Which NetApp storage system is the most appropriate choice for this workload?

A. NetApp C-Series
B. NetApp ASA (All-SAN Array)
C. NetApp E-Series
D. NetApp StorageGRID

Question 4

A media company is building a new generative AI service. The project has two main components:
1. Data Lake & Fine-Tuning: A 300 TB repository of unstructured data (videos, images, text) stored as objects will be used to fine-tune a foundational model. This process requires a scalable, cost-effective storage solution that can integrate with cloud-native data processing tools like Apache Spark.
2. Inference & RAG: The fine-tuned model will be used in a customer-facing application that leverages Retrieval-Augmented Generation (RAG). To ensure low-latency responses, the RAG component requires extremely fast lookups from a 10 TB vector database.
The company needs a solution that optimizes both cost and performance for this entire lifecycle.
Which combination of NetApp technologies provides the most appropriate solution for this scenario?

A. Use NetApp StorageGRID for the data lake and a NetApp ASA system for the vector database.
B. Use a single, large NetApp ASA system for both the object data lake and the vector database.
C. Use NetApp E-Series for the data lake and a NetApp ASA system for the vector database.
D. Use NetApp Cloud Volumes ONTAP for the data lake and NetApp StorageGRID for the vector database.

Question 5

A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)

A. NetApp FabricPool
B. NetApp FlexCache
C. NetApp StorageGRID
D. NetApp SnapMirror
E. NetApp XCP

Solutions: