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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

Question 1

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. Manually create a NetApp Snapshot via System Manager before each pipeline run, and use the NetApp DataOps Toolkit only for the training stage.
B. Use NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.
C. Use a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.
D. Use NetApp SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
E. 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.

Question 2

Which of the following best describes the difference between data lakes, data warehouses, and lakehouses?

A. Data lakes store structured data, data warehouses store unstructured data, and lakehouses store only real-time data.
B. Data lakes store raw, unstructured data, data warehouses store structured data, and lakehouses combine the features of both.
C. Data lakes store metadata, data warehouses store transaction data, and lakehouses store archival data.
D. Data lakes store data in cloud storage, data warehouses store it in traditional databases, and lakehouses store it in external drives.

Question 3

A team has deployed a Retrieval-Augmented Generation (RAG) system to answer customer queries. Recently, users have complained that the answers provided by the chatbot are outdated and do not reflect the latest product updates. An architect investigates and finds the following status log from the RAG pipeline's data ingestion monitor.
Timestamp: 2025-07-11T14:00:00Z
System: RAG Pipeline Monitor
Status: WARNING
Message: Vector DB freshness check failed.
Source data appears stale.
Vector_DB_Last_Update: 2025-06-10T08:00:00Z
Knowledge_Base_Last_Modified: 2025-07-11T13:15:00Z
Data_Sync_Service: BlueXP copy and sync
Sync_Job_Status: Succeeded
Based on the log, what is the most likely cause of the outdated answers?

A. The knowledge base itself has not been updated with the latest product information.
B. The BlueXP copy and sync service is failing to copy data to the staging area.
C. The LLM needs to be fine-tuned with the new product information.
D. The process that converts staged documents into vectors and updates the vector database is not running.

Question 4

An organization is developing a new AI-powered application. The initial phase involves feeding a curated 50 TB dataset of labeled images into a complex neural network, allowing the model to learn and adjust its internal parameters over millions of iterations. The second phase involves deploying this finalized model to a web service where it will process single, user-uploaded images and return a classification in real-time.
Which statement accurately describes these two phases?

A. Both Phase 1 and Phase 2 are examples of training.
B. Phase 1 is training, and Phase 2 is inferencing.
C. Both Phase 1 and Phase 2 are examples of inferencing.
D. Phase 1 is inferencing, and Phase 2 is training.

Question 5

Which of the following are typical requirements for inferencing in AI models? (Choose two)

A. Cloud-based inference only
B. Pre-trained models
C. Extensive training data
D. Loading the model into memory

Solutions: