A great way to start the NetApp Certified AI Expert (AI Expert) preparation is to begin by properly appreciating the role that syllabus and study guide play in the NetApp NS0-901 certification exam. This study guide is an instrument to get you on the same page with NetApp and understand the nature of the NetApp NCAE exam.
Our team of experts has composed this NetApp NS0-901 exam preparation guide to provide the overview about NetApp Artificial Intelligence Expert exam, study material, sample questions, practice exam and ways to interpret the exam objectives to help you assess your readiness for the NetApp AI Expert exam by identifying prerequisite areas of knowledge. We recommend you to refer the simulation questions and practice test listed in this guide to determine what type of questions will be asked and the level of difficulty that could be tested in the NetApp NCAE certification exam.
NetApp NS0-901 Exam Overview:
|
Exam Name
|
Artificial Intelligence Expert |
| Exam Number | NS0-901 AI Expert |
| Exam Price | $250 USD |
| Duration | 90 minutes |
| Number of Questions | 60 |
| Passing Score | 66% |
| Recommended Training | NetApp Training |
| Exam Registration | PEARSON VUE |
| Sample Questions | NetApp NS0-901 Sample Questions |
| Practice Exam | NetApp Certified AI Expert Practice Test |
NetApp NS0-901 Exam Topics:
| Section | Weight | Objectives |
|---|---|---|
| AI overview |
- Demonstrate the ability to train and inference
- 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 |
15% |
| AI lifecycle |
- Determine the differences between predictive AI and generative AI
- 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 |
27% |
| AI Software Architectures |
- Describe AI MLOps/LLMOps ecosystems and general use
- 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 |
18% |
| AI Hardware Architectures |
- Describe data aggregation topologies
- 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 |
18% |
| AI Common Challenges |
- Determine how to size storage and compute for training and inferencing workloads
- C-Series vs. A-Series; GPU memory and chip architectures - 5.2 Describe the solutions for code, data, and model traceability Snapshots and cloning - 5.3 Describe how to access and move data for AI workloads - SnapMirror and FlexCache. XCP, Backup and recovery, CopySync - 5.4 Describe solutions to optimize cost - Storage efficiencies, FabricPool, FlexCache, SnapMirror, Data Infrastructure Insights, Keystone - 5.5 Describe solutions to secure storage for AI workloads - Bad data = bad AI; Autonomous Ransomware Protection, Multi-Admin Verification - 5.6 Describe solutions to maximize performance in AI workloads - How to keep GPUs fully utilized, NetApp product positioning for specific workloads and architectures |
22% |
NetApp AI Expert Exam Description:
Pass the NetApp Certified AI Expert exam to stay ahead of the technology curve. Achieving this certification validates the skills and knowledge associated with NetApp AI solutions and related industry technologies.