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HPE0-V30 Verified 2026 Edition

HPE AI FundamentalsPractice Test

Master the HPE AI Fundamentals with the official PlanetCert Practice Test. Access real exam questions, professional-grade detailed explanations, and our advanced adaptive simulator. Pass your certification exam on the first attempt.

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Exam Information

Official specifications published by HP

Exam Format

90 min
60
65%
HPE ATP - AI solutions (Associate/Technical Professional)

Registration

$91 USD
HPE credential management platform (cp.certmetrics.com/hpe) or online proctoring

HPE0-V30 Exam Topics and Domains

HPE0-V30 is organized into 3 weighted domains.

1

Introduction To GenAI and Industry Specific Applications

44%

Introduction To GenAI and Industry Specific Applications

Transformer Architecture and Attention MechanismIntroduction to Vector DatabasePractical Applications of NLP and Computer Vision Using TransformersConcepts and Use Cases of Large Language ModelsPrompt Engineering and Prompting Techniques
  • Explain transformer architecture and the attention mechanism
  • Describe vector databases and their role in GenAI
  • Apply NLP and computer vision using transformers
  • Explain concepts and use cases of large language models
  • Apply prompt engineering and prompting techniques
2

Data Manipulation-Cleaning and Labelling

44%

Data Manipulation and Advanced GenAI Techniques

Data Manipulation-Cleaning and LabellingRetrieval Augmented GenerationMultimodal Foundation ModelsLangchain and LlamaindexAgents-Design and Implementation
  • Perform data manipulation including cleaning and labelling
  • Explain and apply Retrieval Augmented Generation
  • Describe multimodal foundation models
  • Use LangChain and LlamaIndex frameworks
  • Design and implement AI agents
3

NVIDIA Concepts

12%

NVIDIA Concepts

NVIDIA Concepts

Explain core NVIDIA concepts relevant to AI solutions

How do I earn this certification?

Passing HPE0-V30 earns the HPE ATP - AI solutions certification. It sits in the HPE AI solutions track.

Next Level Options
HPE0-V31 - AI Solutions
Alternative Paths

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How to study for this exam?

The most effective way to prepare for HPE0-V30 is by using the PlanetCert Simulator to practice questions and review detailed explanations.

What's changed on this exam?

Current Status
  • ACTIVE
  • Last content update: 2026-06-16

Who should take this exam?

  • HPE ATP - AI solutions modular curriculum (HPE Tech Pro University)
  • Data science / AI job experience

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Common Questions

What is the HPE0-V30 exam?
HPE0-V30 is the HPE AI Fundamentals exam that earns the HPE ATP - AI solutions certification. It validates introductory competence in designing and implementing AI solutions, including data preparation, rapid/iterative model development, tuning, and deployment.
How many questions are on the HPE0-V30 exam and what is the passing score?
The official HPE datasheet lists 60 questions with a 90-minute duration and a 65% passing score, delivered in English as a proctored exam.
What topics does the HPE0-V30 exam cover?
The exam is weighted 44% Introduction to GenAI and industry-specific applications (transformers, vector databases, NLP/computer vision, LLMs, prompt engineering), 44% Data manipulation (cleaning/labelling, RAG, multimodal models, LangChain and LlamaIndex, agents), and 12% NVIDIA concepts.
What certification does HPE0-V30 lead to and what comes next?
Passing HPE0-V30 earns the HPE ATP - AI solutions certification. The next step is HPE0-V31 (AI Solutions), which earns the HPE ASE - AI solutions certification and requires the ATP as a prerequisite.
How do I register for the HPE0-V30 exam?
HPE has transitioned all certification exam activity to the HPE credential management platform (cp.certmetrics.com/hpe). You need an active HPE account; no separate Pearson VUE access is required for HPE Aruba Networking/Hybrid Cloud-family exams.
Who should take the HPE0-V30 exam?
It is aimed at data professionals such as data scientists, data analysts, AI architects, ML engineers, and DevOps practitioners who want to validate foundational AI and Generative AI knowledge.

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