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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
AI Overview	15%	- Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - AI Deployment Models 1. Edge 2. Benefits and risks of each model 3. Cloud 4. On-premises - Algorithm Types 1. Reinforcement learning 2. Unsupervised learning 3. Supervised learning - AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - Machine Learning Fundamentals 1. Describe machine learning benefits 2. Understand the relationship between AI, machine learning, and deep learning - AI Industry Applications 1. Healthcare applications 2. Digital twins 3. Agents
AI Lifecycle	27%	- Data Preparation 1. NetApp BlueXP Classification 2. Data aggregation and cleansing 3. XCP and CopySync - Generative AI Concepts 1. Retrieval Augmented Generation (RAG) 2. Fine-tuning 3. Hallucinations - Model Development 1. Fine-tuning workflows 2. Inferencing 3. Model building - Predictive AI vs. Generative AI 1. Distinction between predictive and generative AI 2. Impact of generative content (text, images, video, decision-making) 3. Large Language Models (LLMs)
AI Hardware Architectures	18%	- NetApp Architectures 1. BasePod 2. OVX architectures 3. SuperPOD - Networking and Storage 1. Network protocols for AI workloads 2. Storage architectures for AI - Infrastructure Topologies 1. Data aggregation and compute topologies
AI Common Challenges	22%	- Resource Management 1. Sizing storage and compute resources effectively 2. Controlling costs and securing storage - Traceability and Optimization 1. Ensuring traceability for code, data, and models 2. Optimizing data access and movement 3. Maximizing performance in demanding AI workloads
AI Software Architectures	18%	- Scaling and Orchestration 1. Scaling AI workloads with Kubernetes 2. Leveraging BlueXP software tools - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale - Development Tools 1. NetApp DataOps Toolkit 2. Jupyter notebooks vs. pipelines

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
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 automatically terminates all low-priority jobs to free up the entire cluster.
B. It sends an email notification to the administrator to manually reallocate the GPUs.
C. It keeps the high-priority job in a pending state until the low-priority jobs complete naturally.
D. 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.

Answer: D

NEW QUESTION # 71
A data science team works primarily at a central data center but needs to run a short-term, burst- compute training job in the public cloud to take advantage of specialized GPUs that are not available on- premises. They need to efficiently and securely move a 20 TB dataset from their on- premises ONTAP cluster to a Cloud Volumes ONTAP instance for the duration of the job.
The data flow requirement is as follows:
Source: On-premises ONTAP cluster
Destination: Cloud Volumes ONTAP in AWS
Requirement: Efficient, secure, block-level data transfer.
Which NetApp technology is the most appropriate tool for this task?

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

Answer: D

NEW QUESTION # 72
The firm decides to implement a disaster recovery (DR) site for the "Advisor Assistant" application in a secondary data center. The Recovery Point Objective (RPO) is 15 minutes, and the Recovery Time Objective (RTO) is 4 hours. The design must protect both the document data lake and the vector database.
The primary site contains:
- Data Lake: NetApp StorageGRID
- Vector DB: NetApp AFF A-Series
Which combination of technologies and processes provides a complete and robust DR solution?
(Select all that apply.)

A. Rely on tape backups to be shipped to the DR site in the event of a disaster.
B. Use NetApp SnapMirror to create an asynchronous replication relationship for the AFF A-Series volume containing the vector database, with a schedule of 10 minutes.
C. Use BlueXP disaster recovery to orchestrate and automate the failover and failback workflows for the application and its data dependencies.
D. Use NetApp FlexCache at the DR site to cache data from the primary site.
E. Use StorageGRID's built-in replication rules to replicate object data from the primary site's grid to a StorageGRID instance at the DR site.
F. Use the NetApp DataOps Toolkit to manually script the failover process.

Answer: B,C,E

NEW QUESTION # 73
An AI team is embarking on a project to train a new, large-scale computer vision model from scratch. The lead architect emphasizes that the success of the project depends on four fundamental inputs that must be available and managed throughout the training process. Which of the following are the four essential requirements for model generation?

A. Data, code, compute, and time.
B. A pre-trained model, a validation set, an inference engine, and a cloud provider.
C. A data lake, a data warehouse, a data pipeline, and a data mart.
D. A project manager, a data scientist, a software engineer, and a budget.

Answer: A

NEW QUESTION # 74
The pod running the vector database on the Kubernetes cluster fails to start. An MLOps engineer runs 'kubectl describe pod vector-db-pod-0' and sees the following event message:
Events:
Type Reason Age From Message
- - - -
Warning FailedScheduling 30s default-scheduler 0/8 nodes are available: 8 node(s) did not match pod anti-affinity rules.
The pod's manifest contains the following 'affinity' definition:
affinity:
podAntiAffinity:
requiredDuringSchedulingIgnoredDuringExecution:
- labelSelector:
matchExpressions:
- key: app
operator: In
values:
- vector-db
topologyKey: "kubernetes.io/hostname"
What is the most likely reason the pod cannot be scheduled?

A. The 'topologyKey' is invalid; it should be 'failure-domain.beta.kubernetes.io/zone'.
B. The PersistentVolumeClaim for the pod is in a 'Pending' state.
C. The pod's anti-affinity rule prevents it from being scheduled on any node that is already running another pod with the label 'app=vector-db'.
D. The 'vector-db-pod-0' is requesting more GPU resources than are available on any node.

Answer: C

NEW QUESTION # 75
......

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