Free Network Appliance NS0-901 Exam Dumps Questions & Answers

An architect is designing a cost-optimized storage solution for a large AI environment that has two distinct data temperature profiles:
1. Hot Data (200 TB): Actively used training sets and models requiring all-flash performance.
2. Cold Data (2 PB): Inactive, archived datasets and old model versions that must remain online but are infrequently accessed.
The solution must automatically manage data placement based on activity, without requiring manual intervention or changes to file paths. The environment consists of an on-premises data center and a public cloud account.
Which combination of NetApp technologies provides the most complete and cost-effective solution? (Select all that apply.)

• A.Use NetApp XCP to manually script the daily movement of cold files from the AFF system to StorageGRID.
• B.Use NetApp SnapMirror to create a full copy of the 2 PB of cold data on the AFF A-Series for faster access if needed.
• C.Deploy NetApp StorageGRID on-premises to serve as a cost-effective capacity tier for the cold data.
• D.Deploy a single, large Cloud Volumes ONTAP instance to hold both the hot and cold data tiers.
• E.Deploy a NetApp AFF A-Series system on-premises to serve as the high-performance tier for the hot data.
• F.Use NetApp FabricPool to automatically and transparently tier cold data blocks from the AFF A- Series to the StorageGRID system.

An AI architect is reviewing the design for a new data lake. The primary requirement is to store petabytes of unstructured data (images, video, sensor logs) in a highly durable, scalable, and cost- effective manner. The data will be accessed via S3 API by various data processing and analytics applications.
The initial design proposes using a traditional Network Attached Storage (NAS) filer with a large number of disks. The architect reviews the proposal:
Proposed_System: Traditional NAS Filer
Protocol: NFSv4
Scalability_Model: Scale-up
Metadata_Handling: Centralized in filer head
Cost_per_GB: Moderate
Why is this proposed system a poor choice for a petabyte-scale data lake?

• A.The S3 API cannot be used to access data stored on an NFS file system.
• B.A traditional scale-up NAS system will face scalability and cost-effectiveness challenges at the petabyte scale compared to an object storage system.
• C.A NAS filer cannot be deployed on-premises.
• D.NFS is incapable of storing image or video files.

An organization recently suffered a ransomware attack that encrypted several volumes on their primary ONTAP storage system, including a critical volume containing curated training data. The security team needs to implement a solution that can proactively detect and block ransomware- like file I/O patterns and automatically create a secure Snapshot copy before any damage is done.
The current ONTAP configuration is as follows:
ONTAP_Version: 9.12.1
Security_Features: SnapLock (Compliance Mode) on archive volumes
Anti-Virus_Scan: Enabled (Vscan)
Ransomware_Detection: Not configured
Which ONTAP feature should be enabled to provide this proactive, automated protection?

• A.Increase the frequency of scheduled Snapshots.
• B.Enable Autonomous Ransomware Protection (ARP).
• C.Enable Multi-Admin Verification (MAV) on all volumes.
• D.Use BlueXP backup and recovery to back up the volumes to the cloud more frequently.

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 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.
• C.A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.
• D.A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.

A data scientist needs to launch a Jupyter notebook as a pod in a Kubernetes cluster. The pod requires a 50 Gi persistent volume for storing datasets and notebooks. The cluster administrator has configured a default Trident StorageClass for general-purpose use. The data scientist has the following PersistentVolumeClaim (PVC) manifest:
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: jupyter-pvc
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 50Gi
When this PVC is applied to the cluster, what will be the result?

• A.The PVC will fail because a 'storageClassName' is not explicitly defined.
• B.The PVC will remain in a "Pending" state until a PersistentVolume is manually created.
• C.Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.
• D.Trident will create a 1 Gi volume, as this is the default size for all PVCs.