最新のNS0-901問題集で試験を準備する

Network Appliance NS0-901 試験概要:

Network Appliance NS0-901 試験シラバストピック:

Network Appliance NetApp Certified AI Expert 認定 NS0-901 試験問題：

1. A national research laboratory is investing in a turnkey AI infrastructure solution. Their primary goal is to eliminate the complexity and risk of designing and integrating the compute, network, and storage components themselves. The solution must be pre-validated by the vendors to deliver predictable, linear performance as they scale from one to multiple compute nodes. Which two options represent this type of pre-validated, converged infrastructure solution for AI? (Choose two)

A) NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking.
B) A custom-built server rack with components sourced from various different vendors.
C) A single NetApp FAS system connected directly to a single server.
D) A Cisco and NetApp FlexPod for AI solution.
E) A public cloud provider's general-purpose virtual machine instances.

2. The firm decides to expand the "Advisor Assistant" project to a new team in a different department. This team needs its own isolated environment. An MLOps engineer attempts to submit a new GPU- intensive job for the new team, but it remains pending. The engineer checks the Run:AI scheduler logs and finds the following entry:
time="2025-07-11T16:45:00Z" level=info msg="Job ds-new-team-job1 cannot be scheduled.
Project 'new-team-project' has exceeded its GPU quota. Quota: 0, Requested: 1, Used: 0." What is the root cause of the scheduling failure?

A) The Kubernetes cluster has no available GPUs.
B) The Run:AI scheduler is offline and cannot process new jobs.
C) A Run:AI project quota has been configured for the new team, but it has been set to zero, effectively blocking them from using any GPU resources.
D) The job is requesting a specific type of GPU that is not available in the cluster.

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

4. What is the primary difference between C-Series and A-Series storage for AI workloads?

A) C-Series is designed for high-performance workloads, while A-Series focuses on cost-efficiency for general storage.
B) C-Series supports both file and object-based storage, while A-Series is optimized for object storage.
C) A-Series provides GPU-based acceleration, while C-Series only supports CPU-based workloads.
D) A-Series is for smaller-scale AI workloads, while C-Series supports large-scale, high-performance AI applications.

5. An AI operations team is troubleshooting why their RAG-based chatbot is providing outdated information. They have confirmed that the vector database embedding process is functioning correctly, but suspect an issue with the initial data synchronization that moves the knowledge base from an on- premises ONTAP file share to a cloud staging bucket.
They inspect the relevant BlueXP copy and sync job and find the following details:
Service: BlueXP copy and sync
Relationship_Name: KB_Sync_to_Vector_Staging
Source: nfs://ontap-cluster-1/vol_kb/docs
Destination: s3://vector-staging-bucket-89a3/latest/
Last_Sync_Status: FAILED
Last_Sync_Time: 2025-07-11T02:00:15Z
Error_Message: "Authentication error:
Unable to access source.
Check export policy on 'vol_kb'."
Based on this information, what is the most direct solution to fix the data pipeline?

A) Fine-tune the LLM with the latest data instead of using the RAG system.
B) Check the IAM permissions for the role associated with the S3 bucket.
C) Re-run the vector database embedding job.
D) Modify the NFS export policy on the 'vol_kb' volume on the on-premises ONTAP cluster to grant access to the BlueXP Connector.

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