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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

Question 1

A new team of external auditors requires read-only access to the raw financial product documentation stored in the StorageGRID data lake. The data is in a bucket named 'prod-docs'.
The security team must ensure the auditors can list and read objects but cannot write, delete, or modify any data. Which is the most appropriate method to grant this specific access?

A. Create a new StorageGRID tenant account for the auditors with full S3 access.
B. Create a new group and user within StorageGRID, and attach an S3 group policy that explicitly allows only 's3:GetObject' and 's3:ListBucket' actions on the 'prod-docs' bucket.
C. Configure an NFS export policy on the 'prod-docs' bucket and provide the auditors with the mount path.
D. Provide the auditors with the root access keys for the StorageGRID system.

Question 2

The data scientists report that their Kubernetes-based data preparation jobs are failing. The pods are stuck in a 'Pending' state.
An MLOps engineer runs 'kubectl describe pvc data-prep-pvc-01' and sees the following event:
Type Reason Age From Message
- - - -
Warning ProvisioningFailed 2m15s trident-orchestrator-7b... failed to provision volume with StorageClass "bronze-tier": backend unavailable: no healthy backend with satisfying attributes for storage class "bronze-tier" The engineer checks the Trident backend configurations and finds no backend associated with the "bronze-tier" StorageClass.
What is the root cause of the failure?

A. The StorageGRID data lake is offline, preventing the creation of new volumes.
B. The PersistentVolumeClaim is referencing a StorageClass that has no configured or healthy Trident backend to provision storage from.
C. The Kubernetes scheduler is unable to find a node with enough resources for the pods.
D. The data preparation pods do not have the correct security permissions.

Question 3

Due to the success of the "Advisor Assistant," the number of concurrent users is expected to double in the next quarter. The existing Kubernetes cluster is running at 80% of its GPU capacity during peak hours. The architect must propose a plan to scale the compute infrastructure to handle the increased load.
Which two strategies represent the most effective and scalable solutions? (Choose 2.)

A. Horizontally scale the cluster by adding new GPU-equipped nodes.
B. Vertically scale the existing nodes by adding more RAM and CPU cores to each one.
C. Configure the Kubernetes Cluster Autoscaler to automatically add new nodes to the cluster from a predefined node pool when GPU demand exceeds current capacity.
D. Replace all existing GPUs with the next-generation model to increase the performance of each node.
E. Manually deploy all new application replicas to a single, new, very large "super-node".

Question 4

Which of the following applications use AI in the healthcare industry? (Choose two)

A. Diagnosing diseases
B. Predicting patient outcomes
C. Automating financial reporting
D. Managing hospital supply chains

Question 5

An architect is designing a data pipeline for a predictive AI model that will forecast retail sales.
The pipeline must be robust, version-controlled, and efficient.
The proposed data flow is as follows:
1. Ingest: Raw sales data is copied daily from multiple point-of-sale (POS) systems to a central staging area on an on-premises ONTAP cluster.
2. Prepare: The raw data is messy. A data engineering team needs a clean, isolated, and writable copy of the latest daily data to perform cleansing and feature engineering tasks without impacting the original raw data.
3. Train: Once prepared, the cleansed dataset is used to retrain the predictive model on a GPU cluster.
This step must be repeatable with the exact same dataset for compliance.
4. Deploy: The newly trained model is pushed to production inference servers.
Which combination of NetApp technologies best supports this entire predictive AI lifecycle?
(Select all
that apply.)

A. Use NetApp Snapshots on the prepared dataset volume just before training to create an immutable, point-in-time version for compliance and reproducibility.
B. Use NetApp XCP to efficiently aggregate the raw sales data from POS systems into the central staging area.
C. Use NetApp StorageGRID as the primary storage for the high-performance training stage.
D. Use NetApp FlexClone to create an instantaneous, space-efficient, writable copy of the daily raw data for the data preparation stage.
E. Use BlueXP backup and recovery to perform the initial data ingest from the POS systems.
F. Use a RAG architecture for the sales forecasting model.

Solutions: