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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q74-Q79):

NEW QUESTION # 74
Which of the following platforms provides tools for model training and deployment specifically for AI workloads?

A. All of the above
B. Domino Data Labs
C. RunAI
D. Google VertexAI

Answer: A

NEW QUESTION # 75
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 application is experiencing excessive memory paging.
B. The server's CPU is overloaded.
C. The server's network interface is saturated.
D. The storage system (ASA) is unable to provide sufficient read throughput.

Answer: C

NEW QUESTION # 76
An MLOps team uses a variety of platforms to manage their AI workloads. They need to understand the primary function of each tool within their ecosystem. Which statement best describes the role of an MLOps/LLMOps platform like Kubeflow or Run:AI?

A. They are integrated development environments (IDEs) used exclusively for writing Python code.
B. They are specialized storage systems designed to hold large datasets for training.
C. They are networking protocols designed to accelerate data transfer between GPUs.
D. They are orchestration and management platforms that automate and streamline the entire AI/ML lifecycle, from data preparation and model training to deployment and monitoring.

Answer: D

NEW QUESTION # 77
A security administrator is reviewing the configuration of a production ONTAP cluster after a compliance audit. The audit requires that all volumes containing financial data must be protected against ransomware. The administrator runs a command to check the status of Autonomous Ransomware Protection (ARP) on a critical volume.
The command and its output are as follows:
cluster-1::> security ransomware anti-ransomware show -vserver svm_finance -volume finance_q1_data Vserver: svm_finance Volume: finance_q1_data Autonomous Ransomware Protection Status: disabled Learning Mode: normal Dry Run Mode: false Action on Detection: notify Based on this output, what is the primary security risk for the 'finance_q1_data' volume?

A. The action on detection is set only to "notify," which will not block the attack.
B. The learning mode is set to "normal," which may not detect zero-day attacks.
C. The volume is not part of any Snapshot schedule.
D. The volume is vulnerable to ransomware because Autonomous Ransomware Protection is not enabled.

Answer: D

NEW QUESTION # 78
A company is running its AI training workloads on a NetApp AFF A-Series system. To manage costs, they want to automatically move inactive training datasets and older model checkpoints from the high- performance all-flash tier to a lower-cost object storage tier, such as an on- premises StorageGRID or a public cloud bucket. The process must be transparent to the data scientists and not require changes to their scripts or file paths.
Which two NetApp technologies should be combined to achieve this goal? (Choose 2.)

A. NetApp XCP
B. NetApp SnapMirror
C. NetApp FabricPool
D. NetApp StorageGRID
E. NetApp FlexCache

Answer: C,D

NEW QUESTION # 79
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