NetApp NS0-901 Certification Exam Syllabus

• Training, inferencing and predictions

- Describe machine learning benefits

• AI, machine learning, deep learning

- Differentiate the use between different algorithm types

• Supervised, unsupervised, reinforcement

- Describe how AI is used in varied industries

• Digital twins, agents, healthcare

- Describe convergence of AI, high-performance computing, and analytics

• Leveraging the same infrastructure for AI, HPC, and analytics

- Determine the use of AI on-premises, in the cloud, and at the edge

• Benefits, risks

• Industry use of predictive and generative AI

- Describe the impact of predictive AI

• Classification, neural networks, reinforcement, determine preference

- Describe the impact of generative text, images, videos, decisions in Generative AI

• Transformer models, Hallucinations, retrieval augmented generation (RAG) vs. fine-tuning

- Determine how NetApp tools can enable data aggregating, data cleansing, data modeling

• BlueXP classification, XCP, CopySync

- Determine the requirements needed for model generation

• Data, code, compute and time, scenarios

- Compare the differences between model building and fine-tuning models

• Model building = data, code; Fine-tuning = existing model, data, code

- Determine the requirements needed for inferencing

• Loading model into memory (model size); retrieval augmented generation (RAG), or other data lookups (agents), NetApp data mobility solutions

• High-level view of AWS Sagemaker, Google VertexAI, Microsoft AzureML, Domino Data Labs, RunAI, MLflow, KubeFlow, TensorFlow Extended

- Determine the differences between Juypter notebooks vs pipelines

• Notebooks for experimentation, pipelines for iterative development (production)

- Describe how NetApp DataOps toolkit works

• Python; Kubernetes vs. standalone; basic functionality provided by NetApp DataOps Toolkit

- Demonstrate the ability to execute AI workloads at scale with Kubernetes

• Trident

- Describe the uses of BlueXP software tools to build AI solutions

• GenAI Toolkit, Workload Factory, how to securely use private data with Generative AI

• Warehouses, data lakes, and lakehouses

- Describe compute architectures used with AI workloads

• CPU, GPU - Nvidia, TPU, FPGA

- Describe network architecture used with AI workloads

• Ethernet vs. Infiniband; Relevance of RDMA and GPUDirect Storage

- Identify storage architectures used with AI workloads

• C-Series, A-Series, EF-Series, StorageGRID

- Determine the use cases of different protocols

• File, object, parallel file systems, POSIX, clients installed on hosts, etc., file vs object or both; Integrate file data with object-based services (cloud and on-prem), for analytics

- Determine the benefits of SuperPOD architectures with NetApp

• E Series, BeeGFS, integration with enterprise data

- Describe the uses cases for BasePod and OVX architectures

• AIPod, FlexPod AI, OVX

• C-Series vs. A-Series; GPU memory and chip architectures

- Describe the solutions for code, data, and model traceability

• Snapshots and cloning

- Describe how to access and move data for AI workloads

• SnapMirror and FlexCache. XCP, Backup and recovery, CopySync

- Describe solutions to optimize cost

• Storage efficiencies, FabricPool, FlexCache, SnapMirror, Data Infrastructure Insights, Keystone

- Describe solutions to secure storage for AI workloads

• Bad data = bad AI; Autonomous Ransomware Protection, Multi-Admin Verification

- Describe solutions to maximize performance in AI workloads

• How to keep GPUs fully utilized, NetApp product positioning for specific workloads and architectures