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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

1. A data scientist needs to test a new data normalization technique. To do this, they require an isolated, writable copy of a 50 TB curated simulation dataset that resides on the NetApp ASA system. The operation must be completed as quickly as possible and consume minimal additional storage space. Which NetApp technology is the most appropriate solution for this requirement?

A) NetApp FlexClone
B) NetApp FabricPool
C) NetApp SnapMirror
D) NetApp XCP

2. The "Advisor Assistant" application, running as a pod in Kubernetes, suddenly cannot access its data on the AFF A-Series. The application logs show "connection timed out" errors. The network team provides a firewall log snippet for the traffic between the application pod and the storage system's NFS data LIF.
TIME | SRC_IP | DST_IP | PROTO | DST_PORT | ACTION
-|--||-|-|-
2025-07-11T16:01:10Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 111 | BLOCKED 2025-07-
11T16:01:12Z | 10.20.5.101 (Pod) | 10.20.10.55 (LIF) | TCP | 2049 | BLOCKED What is the most likely cause of the connectivity failure?

A) The application pod has incorrect mount options configured.
B) The ONTAP system's data LIF is offline.
C) A network firewall between the pod's subnet and the storage subnet is blocking the required NFS ports.
D) The AFF A-Series does not support the NFS protocol.

3. An architect is explaining different data storage paradigms to a new data engineering team. The team needs to understand where to store different types of data for their AI projects. Which statement accurately describes a key difference between a data lake and a data warehouse?

A) A data lake is only for on-premises deployments, while a data warehouse is only for cloud deployments.
B) A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.
C) A data lake is designed to store vast quantities of raw, multi-format data (structured, semi- structured, and unstructured), while a data warehouse is typically used for storing structured and filtered data that has already been processed for a specific purpose.
D) A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.

4. Which of the following techniques are used to maximize GPU utilization in AI workloads? (Choose two)

A) Utilizing CPU-based workloads for all tasks
B) Scheduling GPU-intensive tasks during off-peak hours
C) Balancing compute and storage workloads efficiently
D) Using larger batch sizes

5. 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 a single, large ReadWriteMany PVC for all stages to simplify the pipeline configuration.
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 SnapMirror for data versioning and manually create hostPath volumes for each pipeline stage.
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 NetApp XCP to copy the data for each stage and configure static PersistentVolumes for each pod.

Solutions: