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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Overview	15%	- AI industry use cases and applications - AI, machine learning, and deep learning concepts - AI deployment models: on-premises, cloud, edge - Convergence of AI, high-performance computing, and analytics - Algorithm types: supervised, unsupervised, reinforcement learning
Topic 2: NetApp AI Solutions and Architecture	25%	- Data management and data pipeline design - Scalability and performance optimization for AI - ONTAP integration with AI frameworks - NetApp AI-ready infrastructure components - Storage architectures for AI workloads
Topic 3: Cloud and Hybrid Cloud AI Deployment	18%	- NetApp cloud data services for AI - Data mobility and consistency across environments - Hybrid and multi-cloud AI architectures - Cloud-native AI solutions and integration
Topic 4: AI Lifecycle	27%	- Predictive vs generative AI - AI governance, ethics, and compliance - Model training, inference, and optimization - Data preparation and management for AI - AI lifecycle stages: design, training, deployment, monitoring
Topic 5: Security, Reliability, and Operations	15%	- High availability and data protection - Cost management and efficiency - Data security and access control for AI - Monitoring, logging, and troubleshooting AI environments

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q43-Q48):

NEW QUESTION # 43
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. 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.
B. Fine-tune the LLM on a sanitized dataset to teach it to ignore PII.
C. Move the entire client portfolio database to an encrypted, air-gapped system.
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: A,D

NEW QUESTION # 44
An organization's AI platform team needs to provide two distinct tiers of storage for their data scientists on a single Kubernetes cluster:
1. 'gold-tier': Extremely low-latency storage for active model training, using an all-flash NetApp ASA system.
2. 'bronze-tier': Cost-effective, high-capacity storage for data staging and archiving, using a NetApp StorageGRID system.
How should the platform team configure NetApp Trident to meet these requirements? (Select all that apply.)

A. Configure the 'gold-tier' StorageClass to reference the ASA backend.
B. Configure the 'bronze-tier' StorageClass to reference the StorageGRID backend.
C. Create two separate Trident backend configurations, one for the ASA ('backend-asa.yaml') and one for StorageGRID ('backend-sgrid.yaml').
D. Create a single Trident backend that points to both the ASA and StorageGRID systems simultaneously.
E. Create a single StorageClass named 'multi-tier-storage' that references both backends.
F. Create two distinct StorageClasses, 'gold-tier' and 'bronze-tier'.

Answer: A,B,C,F

NEW QUESTION # 45
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 network interface is saturated.
B. The server's CPU is overloaded.
C. The storage system (ASA) is unable to provide sufficient read throughput.
D. The application is experiencing excessive memory paging.

Answer: A

NEW QUESTION # 46
An architect is explaining different data storage paradigms to a new data engineering team. The team needs to understand where to store different types of data for their AI projects. Which statement accurately describes a key difference between a data lake and a data warehouse?

A. A data lake stores structured, processed data for fast querying, while a data warehouse stores raw, unstructured data in its native format.
B. A data lake uses a schema-on-write approach, while a data warehouse uses a schema-on-read approach.
C. A data lake is designed to store vast quantities of raw, multi-format data (structured, semi- structured, and unstructured), while a data warehouse is typically used for storing structured and filtered data that has already been processed for a specific purpose.
D. A data lake is only for on-premises deployments, while a data warehouse is only for cloud deployments.

Answer: C

NEW QUESTION # 47
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 custom-built server rack with components sourced from various different vendors.
B. A single NetApp FAS system connected directly to a single server.
C. NetApp AIPod, which integrates NVIDIA DGX servers with NetApp storage and networking.
D. A public cloud provider's general-purpose virtual machine instances.
E. A Cisco and NetApp FlexPod for AI solution.

Answer: C,E

NEW QUESTION # 48
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