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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Objectives
Topic 1: AI Infrastructure and NetApp Solutions	- AI-ready data infrastructure 1. High-performance storage for AI workloads 2. Data pipeline optimization for AI/ML workloads - Converged workloads 1. AI, HPC, and analytics convergence 2. Shared infrastructure design considerations
Topic 2: AI and Machine Learning Fundamentals	- Algorithm types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning - AI, ML, DL concepts 1. Differences between AI, machine learning, and deep learning 2. Training, inference, and prediction workflows
Topic 3: Industry Use Cases	- AI applications across industries 1. Healthcare AI 2. Autonomous systems and agents 3. Digital twins
Topic 4: AI Lifecycle and Deployment	- Operational challenges 1. Data governance and management 2. Scalability and performance optimization - End-to-end AI lifecycle 1. Data ingestion and preparation 2. Model deployment and monitoring 3. Model training and validation

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q39-Q44):

NEW QUESTION # 39
An AI infrastructure team is selecting a storage protocol for a new, high-performance computing (HPC) cluster that will be used for a weather modeling AI application. The application requires extremely high-throughput, parallel access from hundreds of compute nodes to a shared dataset.
The workload characteristics are as follows:
Access_Pattern: Massively parallel reads from many clients.
File_Type: Large, shared data files.
Latency_Sensitivity: High (for metadata operations).
Primary_Requirement: Maximum aggregate throughput.
Which storage protocol is best suited for this workload?

A. A parallel file system like BeeGFS or Lustre.
B. An object-based storage system using S3.
C. A standard file-based system using NFS.
D. A block-based protocol using iSCSI.

Answer: A

NEW QUESTION # 40
Given the company's goal of combining physics-based simulations with AI-driven analytics on a shared data foundation, which industry trend does this project best represent?

A. The separation of AI and HPC into dedicated, air-gapped environments.
B. The exclusive use of public cloud resources for all computational tasks.
C. The replacement of all physical testing with digital simulations.
D. The convergence of AI, High-Performance Computing (HPC), and analytics on a unified data infrastructure.

Answer: D

NEW QUESTION # 41
The firm's data science team needs to run a high-priority, interactive model analysis job that requires immediate access to two GPUs. However, all GPUs in the cluster are currently allocated to long-running, lower-priority batch training jobs.
The MLOps platform, Run:AI, shows the following queue status:
JOB_ID | PROJECT | STATUS | PRIORITY | GPU_ALLOCATED
||--|-|
batch_job_1 | team_a | Running | Low | 2
batch_job_2 | team_a | Running | Low | 2
batch_job_3 | team_b | Running | Low | 4
interactive_1| team_c | Pending | High | 2 (requested)
How does the Run:AI platform address this resource contention to allow the high-priority job to run?

A. It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
B. It automatically terminates all low-priority jobs to free up the entire cluster.
C. It automatically pauses one of the low-priority jobs, saves its state, and allocates its GPUs to the high- priority job, placing the paused job back in the queue.
D. It sends an email notification to the administrator to manually reallocate the GPUs.

Answer: C

NEW QUESTION # 42
To comply with the security mandate, the architect must design a process to prevent client PII from the portfolio database from ever being included in the context sent to the LLM. Which two actions are required to build a robust and automated solution for this? (Choose 2.)

A. Move the entire client portfolio database to an encrypted, air-gapped system.
B. Implement a data preparation step in the RAG pipeline that programmatically masks or excludes any data fields tagged as PII before the context is constructed.
C. Fine-tune the LLM on a sanitized dataset to teach it to ignore PII.
D. Use BlueXP classification to perform an initial scan of the portfolio database to identify and tag all columns containing PII.
E. Rely on a disclaimer in the chatbot UI telling advisors not to enter queries related to specific clients.

Answer: B,D

NEW QUESTION # 43
The data pipeline that updates the vector database from the StorageGRID data lake fails. An MLOps engineer uses the BlueXP API to check the status of the copy and sync job responsible for moving new documents to a staging area for processing.
The API returns the following JSON response:
{
"id": "rel_5f8g7h2k",
"volumeName": "product_docs_source",
"svmName": "svm_datalake",
"relationshipStatus": "FAILED",
"s3Location": "s3://vector-staging-bucket/latest/",
"progress": 87,
"failureMessage": "Sync failed.
Target credentials invalid. Check permissions for data broker 'db-gcp-1' on target bucket 'vector- staging-bucket'."
}
Based on this output, what is the most direct cause of the pipeline failure?

A. The vector database embedding process has crashed.
B. The network connection between the data broker and the StorageGRID system is blocked by a firewall.
C. The BlueXP data broker service account or role lacks the necessary IAM permissions to write to the target S3 bucket.
D. The source StorageGRID system is offline.

Answer: C

NEW QUESTION # 44
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