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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Deep Learning | - CNN and RNN Architectures - Neural Network Fundamentals - Model Training and Optimization |
| AI Application Development (EI) | - Building AI Applications - AI Service Integration - Enterprise Intelligence (EI) Concepts |
| Huawei AI Ecosystem Tools | - AI Development Toolchain - Huawei Cloud AI Services - MindSpore Framework Basics |
| AI Fundamentals | - Common AI Use Cases in Industry - Introduction to Artificial Intelligence - AI Development Lifecycle |
| Model Deployment and Operations | - Model Deployment Strategies - Monitoring and Maintenance - Inference Services |
| Model Development with Huawei ModelArts | - ModelArts Platform Overview - AutoML Capabilities - Training Models on ModelArts |
| Machine Learning | - Supervised Learning
|
| Data Processing | - Feature Engineering - Data Collection and Cleaning - Data Labeling and Preparation |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
If a scanned document is not properly placed, and the text is tilted, it is difficult to recognize the characters in the document. Which of the following techniques can be used for correction in this case?
- A. Affine transformation
- B. Grayscale transformation
- C. Rotational transformation
- D. Perspective transformation
Correct Answer: A,C 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
In NLP tasks, transformer models perform well in multiple tasks due to their self-attention mechanism and parallel computing capability. Which of the following statements about transformer models are true?
- A. Multi-head attention is the core component of a transformer model. It computes multiple attention heads in parallel to capture semantic information in different subspaces.
- B. Transformer models outperform RNN and CNN in processing long texts because they can effectively capture global dependencies.
- C. A transformer model directly captures the dependency between different positions in the input sequence through the self-attention mechanism, without using the recurrent neural network (RNN) or convolutional neural network (CNN).
- D. Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.
Correct Answer: A,B,C 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
The development of large models should comply with ethical principles to ensure the legal, fair, and transparent use of data.
- A. FALSE
- B. TRUE
Correct Answer: B 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
Which of the following statements about the multi-head attention mechanism of the Transformer are true?
- A. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
- B. The concatenated output is fed directly into the multi-headed attention mechanism.
- C. Each header's query, key, and value undergo a shared linear transformation to obtain them.
- D. The multi-head attention mechanism captures information about different subspaces within a sequence.
Correct Answer: A,D 🗳️
The attention mechanism in foundation model architectures allows the model to focus on specific parts of the input data. Which of the following steps are key components of a standard attention mechanism?
- A. Calculate the dot product similarity between the query and key vectors to obtain attention scores.
- B. Compute the weighted sum of the value vectors using the attention weights.
- C. Normalize the attention scores to obtain attention weights.
- D. Apply a non-linear mapping to the result obtained after the weighted summation.
Correct Answer: A,B,C 🗳️
Explanation: Only visible for Exam4Free members. You can sign-up / login (it's free).
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