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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

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

2. What is the primary role of NetApp Trident in a Kubernetes environment designed for AI workloads?

A) To function as a container runtime interface for executing AI models.
B) To directly accelerate GPU computations using specialized drivers.
C) To act as a dynamic storage orchestrator, provisioning persistent storage from NetApp backends on- demand for containerized applications.
D) To provide a web-based IDE, like Jupyter, for data scientists to develop models.

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

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

5. Which of the following best describes the difference between data lakes, data warehouses, and lakehouses?

A) Data lakes store raw, unstructured data, data warehouses store structured data, and lakehouses combine the features of both.
B) Data lakes store metadata, data warehouses store transaction data, and lakehouses store archival data.
C) Data lakes store data in cloud storage, data warehouses store it in traditional databases, and lakehouses store it in external drives.
D) Data lakes store structured data, data warehouses store unstructured data, and lakehouses store only real-time data.

Solutions: