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How much Huawei Certified ICT Associate-Artificial Intelligence cost
Huawei Certified ICT Associate-Artificial Intelligence cost is $250 USD
The HCIA-AI certification exam consists of multiple-choice questions and practical exercises that assess a candidate's knowledge and skills in AI. H13-311-ENU exam covers a range of topics, including AI technologies and applications, machine learning algorithms, deep learning models, and natural language processing techniques. Candidates who pass the exam will be awarded the HCIA-AI certification, which is recognized by employers and organizations around the world as a mark of excellence in the field of AI.
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Huawei H13-311-ENU exam is made up of 60 multiple-choice questions, and the total exam duration is 90 minutes. H13-311-ENU exam is scored on a scale of 1000, and candidates must score at least 600 to pass. H13-311-ENU exam covers topics such as AI introduction and concepts, machine learning, deep learning, and neural networks. Passing the Huawei H13-311-ENU Certification Exam is a mark of distinction and a testament to your effort, dedication, and knowledge in the field of AI.
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Huawei H13-311-ENU HCIA-AI (Huawei Certified ICT Associate-Artificial Intelligence) exam is a 90-minute test that consists of 60 multiple-choice questions. H13-311-ENU exam requires a good understanding of statistics and mathematics since it will test an individual's ability to calculate, interpret and apply the techniques and methods of artificial intelligence. Moreover, this certification is beneficial for individuals planning to pursue a career in data science, machine learning, or artificial intelligence. It will equip them with the necessary skills and knowledge to be competitive within the job market.
Huawei H13-311-ENU Exam Syllabus Topics:
| Section | Weight | Objectives |
| Introduction to Huawei AI Platforms | 14% | - Huawei AI Ecosystem
- 1. Huawei Cloud AI Services
- 2. Ascend AI Processors
- 3. CANN
- 4. Atlas Products
- 5. ModelArts
|
| AI Overview | 15% | - Artificial Intelligence Fundamentals
- 1. AI Application Scenarios
- 2. AI Concepts and Development History
- 3. AI Industry Trends
|
| Deep Learning Overview | 25% | - Deep Learning Fundamentals
- 1. Transformer Models
- 2. Convolutional Neural Networks
- 3. Recurrent Neural Networks
- 4. Neural Networks
|
| Cutting-edge AI Applications | 6% | - Emerging AI Technologies
- 1. Large Language Models
- 2. AI Ethics and Governance
- 3. Generative AI
|
| Machine Learning Overview | 20% | - Machine Learning Fundamentals
- 1. Model Training and Evaluation
- 2. Supervised Learning
- 3. Common Machine Learning Algorithms
- 4. Unsupervised Learning
|
| AI Development Framework | 20% | - AI Development Technologies
- 1. AI Model Deployment
- 2. Python for AI
- 3. Model Development Workflow
- 4. MindSpore Framework
|