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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Model Development with Huawei ModelArts | - AutoML Capabilities - Training Models on ModelArts - ModelArts Platform Overview |
| Topic 2: Model Deployment and Operations | - Model Deployment Strategies - Monitoring and Maintenance - Inference Services |
| Topic 3: Huawei AI Ecosystem Tools | - Huawei Cloud AI Services - AI Development Toolchain - MindSpore Framework Basics |
| Topic 4: AI Application Development (EI) | - AI Service Integration - Building AI Applications - Enterprise Intelligence (EI) Concepts |
| Topic 5: AI Fundamentals | - AI Development Lifecycle - Introduction to Artificial Intelligence - Common AI Use Cases in Industry |
| Topic 6: Deep Learning | - Model Training and Optimization - Neural Network Fundamentals - CNN and RNN Architectures |
| Topic 7: Data Processing | - Data Collection and Cleaning - Data Labeling and Preparation - Feature Engineering |
| Topic 8: Machine Learning | - Unsupervised Learning
|
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
1. Seq2Seq is a model that translates one sequence into another sequence, essentially consisting of two recurrent neural networks (RNNs), one is the Encoder, and the other is the ---------. (Fill in the blank.)
2. Which of the following are the impacts of the development of large models?
A) Data privacy and security issues will be exacerbated
B) Large models will completely replace small and domain-specific models
C) Model pre-training costs will be reduced
D) The accuracy and efficiency of natural language processing tasks will improve
3. Which of the following statements about the multi-head attention mechanism of the Transformer are true?
A) Each header's query, key, and value undergo a shared linear transformation to obtain them.
B) The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
C) The concatenated output is fed directly into the multi-headed attention mechanism.
D) The multi-head attention mechanism captures information about different subspaces within a sequence.
4. Maximum likelihood estimation (MLE) requires knowledge of the sample data's distribution type.
A) TRUE
B) FALSE
5. A text classification task has only one final output, while a sequence labeling task has an output in each input position.
A) TRUE
B) FALSE
Solutions:
| Question # 1 Answer: Only visible for members | Question # 2 Answer: A,D | Question # 3 Answer: B,D | Question # 4 Answer: A | Question # 5 Answer: A |
Murray -
The 2-3 simulation questions in the beginning of the H13-321_V2.5 exam don't count towards your overall score. Just skip them. I passed with a perfect 900 using H13-321_V2.5 dumps from here.