Network Appliance NS0-901問題サンプル、NS0-901資格講座

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Network Appliance NS0-901 Exam Syllabus Topics:

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

>> Network Appliance NS0-901問題サンプル <<

NS0-901資格講座、NS0-901トレーニング資料

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Network Appliance NetApp Certified AI Expert Exam 認定 NS0-901 試験問題 (Q82-Q87):

質問 # 82
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 network connection between the data broker and the StorageGRID system is blocked by a firewall.
B. The vector database embedding process has crashed.
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.

正解：C

質問 # 83
An AI platform is suffering from poor performance during distributed training jobs. The training data resides on a single, large NFS volume. Monitoring shows that while the overall network throughput to the storage system is high, individual GPU nodes experience significant I/O wait times, and the single ONTAP volume is becoming a performance bottleneck. The goal is to re- architect the storage layout to maximize read parallelism and throughput for the training cluster.
Which two actions should the architect take to address this performance bottleneck? (Choose 2.)

A. Use NetApp FlexCache to create a local cache of the training data on each compute node.
B. Enable QoS maximums on the training volume to limit its IOPS.
C. Implement a NetApp FlexGroup volume to spread the dataset across multiple constituent volumes and aggregates.
D. Increase the number of network ports connected to the storage controller.
E. Replace the NFS protocol with iSCSI for all training data access.

正解：A、C

A. Both projects should use NetApp StorageGRID as the primary storage for training to ensure low latency.
B. Both projects are examples of fine-tuning and have similar infrastructure needs.
C. Project A requires only a large amount of data, while Project B requires only a large amount of code.
D. Project A is a model building task and requires a significantly larger and more powerful compute and storage infrastructure than Project B.
E. Project B is a fine-tuning task, which leverages an existing model and requires less data and compute resources than building a model from scratch.

正解：D、E

質問 # 85
The architect needs to design an efficient data flow to move curated training sets from the central StorageGRID data lake to the high-performance NetApp ASA system used by the AI cluster. The process must be manageable from a single interface and should be automatable. Which two NetApp technologies should be used to implement this data pipeline stage? (Choose 2.)

A. NetApp FabricPool to automatically tier the data from StorageGRID to the ASA.
B. A manual copy process using an S3 client on a bastion host.
C. A custom Python script using the NetApp DataOps Toolkit to trigger the data movement.
D. NetApp SnapMirror to replicate data from the StorageGRID object store to the ASA's file system.
E. NetApp BlueXP copy and sync service to create and manage the data synchronization relationship between the S3 source and the NFS/CIFS destination.

正解：C、E

質問 # 86
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. Fine-tune the LLM on a sanitized dataset to teach it to ignore PII.
B. Move the entire client portfolio database to an encrypted, air-gapped system.
C. Use BlueXP classification to perform an initial scan of the portfolio database to identify and tag all columns containing PII.
D. Rely on a disclaimer in the chatbot UI telling advisors not to enter queries related to specific clients.
E. 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.

正解：C、E

質問 # 87
......

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