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Network Appliance NS0-901 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: AI Software Architectures	18%	- Scaling and Orchestration 1. Leveraging BlueXP software tools 2. Scaling AI workloads with Kubernetes - Development Tools 1. Jupyter notebooks vs. pipelines 2. NetApp DataOps Toolkit - MLOps and LLMOps Ecosystems 1. Understanding the software tools and platforms enabling AI at scale
Topic 2: AI Lifecycle	27%	- Data Preparation 1. Data aggregation and cleansing 2. NetApp BlueXP Classification 3. XCP and CopySync - Model Development 1. Model building 2. Fine-tuning workflows 3. Inferencing - Generative AI Concepts 1. Retrieval Augmented Generation (RAG) 2. Hallucinations 3. Fine-tuning - Predictive AI vs. Generative AI 1. Large Language Models (LLMs) 2. Impact of generative content (text, images, video, decision-making) 3. Distinction between predictive and generative AI
Topic 3: AI Hardware Architectures	18%	- Networking and Storage 1. Storage architectures for AI 2. Network protocols for AI workloads - Infrastructure Topologies 1. Data aggregation and compute topologies - NetApp Architectures 1. OVX architectures 2. BasePod 3. SuperPOD
Topic 4: AI Common Challenges	22%	- Traceability and Optimization 1. Optimizing data access and movement 2. Maximizing performance in demanding AI workloads 3. Ensuring traceability for code, data, and models - Resource Management 1. Controlling costs and securing storage 2. Sizing storage and compute resources effectively
Topic 5: AI Overview	15%	- AI Convergence with HPC and Analytics 1. Leveraging shared infrastructure for AI, HPC, and analytics - AI Deployment Models 1. Cloud 2. Benefits and risks of each model 3. Edge 4. On-premises - Algorithm Types 1. Supervised learning 2. Reinforcement learning 3. Unsupervised learning - Training vs. Inferencing vs. Predictions 1. Distinguish between training and inference workloads - AI Industry Applications 1. Agents 2. Healthcare applications 3. Digital twins - Machine Learning Fundamentals 1. Understand the relationship between AI, machine learning, and deep learning 2. Describe machine learning benefits

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Network Appliance NetApp Certified AI Expert Exam NS0-901 Prüfungsfragen mit Lösungen (Q19-Q24):

19. Frage
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 storage system (ASA) is unable to provide sufficient read throughput.
C. The server's CPU is overloaded.
D. The application is experiencing excessive memory paging.

Antwort: A

20. Frage
A data scientist needs to launch a Jupyter notebook as a pod in a Kubernetes cluster. The pod requires a 50 Gi persistent volume for storing datasets and notebooks. The cluster administrator has configured a default Trident StorageClass for general-purpose use. The data scientist has the following PersistentVolumeClaim (PVC) manifest:
apiVersion: v1
kind: PersistentVolumeClaim
metadata:
name: jupyter-pvc
spec:
accessModes:
- ReadWriteOnce
resources:
requests:
storage: 50Gi
When this PVC is applied to the cluster, what will be the result?

A. The PVC will fail because a 'storageClassName' is not explicitly defined.
B. The PVC will remain in a "Pending" state until a PersistentVolume is manually created.
C. Trident will automatically provision a 50 Gi volume on its default backend and bind it to the PVC.
D. Trident will create a 1 Gi volume, as this is the default size for all PVCs.

Antwort: C

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

Antwort: A

22. Frage
Which of the following best describes the difference between data lakes, data warehouses, and lakehouses?

A. Data lakes store data in cloud storage, data warehouses store it in traditional databases, and lakehouses store it in external drives.
B. Data lakes store raw, unstructured data, data warehouses store structured data, and lakehouses combine the features of both.
C. Data lakes store structured data, data warehouses store unstructured data, and lakehouses store only real-time data.
D. Data lakes store metadata, data warehouses store transaction data, and lakehouses store archival data.

Antwort: B

23. Frage
In the data pipeline, which NetApp technology is used to create an efficient, block-level, incremental replication of the anonymized datasets from the on-premises ASA to the Cloud Volumes ONTAP instance in Azure?

A. NetApp XCP
B. NetApp FabricPool
C. NetApp BlueXP copy and sync
D. NetApp SnapMirror

Antwort: D

24. Frage
......

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