NS0-901 : NetApp Certified AI Expert Exam

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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. An AI architect is designing a solution for a legal firm. The primary goal is to allow lawyers to ask natural language questions about case law stored in a private, 50 TB document repository.
The key project constraints are as follows:
Project_Goal: Answer questions using proprietary, real-time legal documents.
Constraint_1: Must not alter the foundational LLM's weights due to compliance.
Constraint_2: Case law database is updated daily with new rulings.
Constraint_3: All generated answers must be traceable to a source document.
Which technology should the architect choose as the core of this solution?

A) A fine-tuning pipeline to update the LLM daily.
B) A new LLM trained from scratch on the legal documents.
C) A predictive AI model to classify legal documents.
D) A Retrieval-Augmented Generation (RAG) architecture.

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

A) C-Series supports both file and object-based storage, while A-Series is optimized for object storage.
B) C-Series is designed for high-performance workloads, while A-Series focuses on cost-efficiency for general 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.

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

4. A training job on one of the NVIDIA DGX servers is running slowly. A performance engineer runs the 'dstat' command on the server and captures the following output during the job execution.
-total-cpu-usage- -dsk/total- -net/total- paging-- system-- usr sys idl wai hiq siq| read writ| recv send| in out | int csw 15 5 70 10 0 0| 1.2G 15.0M| 1.2G 12.0M| 0 0 | 15k 35k
14 6 69 11 0 0| 1.2G 14.3M| 1.2G 11.8M| 0 0 | 14k 33k
16 5 68 11 0 0| 1.2G 16.1M| 1.2G 13.1M| 0 0 | 16k 36k
The server is connected to the NetApp ASA via a 100GbE (12.5 GB/s) network.
What is the most likely performance bottleneck based on this data?

A) The server's CPU is overloaded.
B) The application is experiencing excessive memory paging.
C) The server's network interface is saturated.
D) The storage system (ASA) is unable to provide sufficient read throughput.

5. An organization has a core data center with a large AI training cluster and several remote edge locations for data ingest and local inference. The edge locations frequently need access to the latest models trained in the core data center, but WAN bandwidth is limited and can be unreliable.
Users at the edge are reporting slow model loading times.
An architect reviews the data access logs from an edge site:
Timestamp: 2025-07-11T15:30:00Z
Event: Model_Load_Request
Model_Path: nfs://core-filer.example.com/vol/models/latest_model.pkl
Source_IP: 192.168.100.15 (Edge Server)
Destination_IP: 10.1.1.50 (Core Filer)
Status: SUCCESS
Duration: 3600s (60 minutes)
What is the most likely cause of the slow model loading times at the edge?

A) The core ONTAP filer is using slow, capacity-based disks.
B) The NFS version used between the core and edge is outdated.
C) The edge server does not have enough RAM to cache the model effectively.
D) The model file is being transferred over a slow, high-latency WAN link for every load request.

質問と回答：