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Network Appliance NS0-901 Exam Syllabus Topics:

Network Appliance NetApp Certified AI Expert Sample Questions:

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

2. An AI architect needs to design a complete, end-to-end data pipeline for a new generative AI application at a financial services firm. The application will allow internal analysts to query a massive, 500 TB archive of historical market data and reports to generate summaries. The firm has the following environment and requirements:
Data_Sources: A mix of on-premises ONTAP filers and StorageGRID S3 buckets.
Requirement_1: All queries must be answered using only the private data archive.
Requirement_2: All generated summaries must provide citations to the source reports.
Requirement_3: All data containing client PII must be identified and excluded from the LLM context.
Requirement_4: The solution must be cost-effective for the large, mostly-read data archive.
Which set of actions and technologies constitutes the most robust and compliant solution?
(Select all that apply.)

A) Implement a Retrieval-Augmented Generation (RAG) architecture to meet the requirements for private data usage and source citation.
B) Deploy BlueXP classification to scan the entire StorageGRID data lake to identify and tag all files containing PII.
C) Use SnapMirror to replicate the StorageGRID data lake to a high-performance NetApp ASA system for faster query performance.
D) Fine-tune a foundation model on the entire 500 TB dataset to ensure it understands the financial context.
E) Use NetApp XCP to perform a one-time migration of all data from the ONTAP filers to the StorageGRID data lake.
F) During the RAG retrieval step, filter out any documents tagged as containing PII by BlueXP classification before sending them to the LLM.

3. The HPC cluster generates simulation data at an extremely high rate, requiring a storage system that can handle massively parallel writes from hundreds of compute nodes simultaneously. Which storage system and file protocol combination is the most appropriate choice for the HPC cluster's high-performance scratch space?

A) A NetApp E-Series system serving a BeeGFS parallel file system.
B) A Cloud Volumes ONTAP instance with a standard file system.
C) A NetApp StorageGRID system accessed via the S3 protocol.
D) A NetApp ASA system serving a single, large NFS volume.

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

5. A data scientist is using the NetApp DataOps Toolkit for Python to automate the creation of a new, writable volume for an experiment. The script is intended to clone an existing dataset volume. When the script is executed, it fails with an error.
The relevant portion of the Python script is:
from netapp_dataops.k8s import clone_pvc
clone_pvc(
source_pvc_name="dataset-v1-pvc",
new_pvc_name="experiment-clone-pvc",
namespace="ds-team-1"
)
The script produces the following error in the terminal:
'Error: Failed to clone PVC. Source PVC 'dataset-v1-pvc' not found in namespace 'ds-team-1'.' What is the most likely cause of this error?

A) The Python script is missing the necessary import statement for the toolkit.
B) The Kubernetes cluster does not have NetApp Trident installed.
C) The source PersistentVolumeClaim (PVC) named 'dataset-v1-pvc' does not exist or is in a different namespace.
D) The NetApp DataOps Toolkit does not support cloning volumes.

Solutions: