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Network Appliance NS0-901 Exam Syllabus Topics:

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

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Network Appliance NetApp Certified AI Expert Exam Sample Questions (Q40-Q45):

NEW QUESTION # 40
An AI training job is running slower than expected. The infrastructure team suspects a compute bottleneck. The job involves processing high-resolution images with a complex convolutional neural network (CNN). They review the logs from the training script.
Epoch 1/100 - 3600s - loss: 1.253 - acc: 0.54
...
CPU_Utilization_During_Epoch: 99% (all cores)
GPU_0_Utilization_During_Epoch: 8%
GPU_1_Utilization_During_Epoch: 7%
...
Epoch 2/100 - 3610s - loss: 1.102 - acc: 0.61
What is the most likely cause of the poor training performance?

A. The model is too simple and does not effectively utilize the GPU's parallel processing capabilities.
B. The training script is CPU-bound, likely performing data augmentation or preprocessing on the CPU instead of offloading it to the GPUs.
C. The storage system cannot deliver data fast enough to the compute node.
D. The network is saturated, preventing the GPUs from receiving data.

Answer: B

NEW QUESTION # 41
The company decides to establish a disaster recovery (DR) site in a secondary data center for the entire Digital Twin platform. The DR plan must protect the HPC data, the AI training data, and the central data lake.
The DR requirements are:
- RPO: 4 hours for all data.
- RTO: 24 hours for the entire platform.
- Process: The failover and failback process should be as automated as possible.
Which combination of technologies provides the most comprehensive DR solution?

A. Deploy a MetroCluster configuration spanning the primary and DR sites for all three storage systems.
B. Use NetApp SnapMirror for the ASA, StorageGRID replication for the data lake, and rely on tape backups for the E-Series/BeeGFS data.
C. Use NetApp SnapMirror for the ASA, StorageGRID's built-in replication for the data lake, and a host- based tool like 'rsync' for the E-Series/BeeGFS data, all orchestrated via BlueXP disaster recovery.
D. Use BlueXP backup and recovery to back up all three storage systems (ASA, E-Series, StorageGRID) to a single cloud target.

Answer: C

NEW QUESTION # 42
A new team of external auditors requires read-only access to the raw financial product documentation stored in the StorageGRID data lake. The data is in a bucket named 'prod-docs'.
The security team must ensure the auditors can list and read objects but cannot write, delete, or modify any data. Which is the most appropriate method to grant this specific access?

A. Create a new group and user within StorageGRID, and attach an S3 group policy that explicitly allows only 's3:GetObject' and 's3:ListBucket' actions on the 'prod-docs' bucket.
B. Provide the auditors with the root access keys for the StorageGRID system.
C. Configure an NFS export policy on the 'prod-docs' bucket and provide the auditors with the mount path.
D. Create a new StorageGRID tenant account for the auditors with full S3 access.

Answer: A

NEW QUESTION # 43
The firm decides to expand the "Advisor Assistant" project to a new team in a different department. This team needs its own isolated environment. An MLOps engineer attempts to submit a new GPU- intensive job for the new team, but it remains pending. The engineer checks the Run:AI scheduler logs and finds the following entry:
time="2025-07-11T16:45:00Z" level=info msg="Job ds-new-team-job1 cannot be scheduled.
Project 'new-team-project' has exceeded its GPU quota. Quota: 0, Requested: 1, Used: 0." What is the root cause of the scheduling failure?

A. The Run:AI scheduler is offline and cannot process new jobs.
B. A Run:AI project quota has been configured for the new team, but it has been set to zero, effectively blocking them from using any GPU resources.
C. The Kubernetes cluster has no available GPUs.
D. The job is requesting a specific type of GPU that is not available in the cluster.

Answer: B

NEW QUESTION # 44
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 only for on-premises deployments, while a data warehouse is only for cloud deployments.
D. 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.

Answer: D

NEW QUESTION # 45
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