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Huawei H13-321_V2.5 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Overview of Huawei's AI Development Strategy and Full-Stack, All-Scenario AI Portfolio2%- All-scenario AI solutions
- Full-stack AI technology system
- Huawei AI development layout
Topic 2: Image Processing Lab Guide12%- Performance optimization and testing
- Development environment setup
- Image processing model development and deployment
Topic 3: Theoretical Knowledge and Applications of Speech Processing10%- Application cases
- Speech signal processing foundation
- Speech feature extraction
- Speech recognition and synthesis
Topic 4: Speech Processing Lab Guide12%- Speech model building and tuning
- Application deployment and verification
- Speech data processing practice
Topic 5: Theoretical Knowledge and Applications of Natural Language Processing10%- Machine translation, text generation and other technologies
- Practical application
- Language model and semantic understanding
- Text processing and representation
Topic 6: Overview of ModelArts4%- Basic operation process
- Core functions and service modules
- ModelArts positioning and architecture
Topic 7: Theoretical Knowledge and Applications of Image Processing26%- Feature extraction and representation
- Typical application scenarios
- Image classification, detection and segmentation
- Image preprocessing technology
Topic 8: Neural Network Basics4%- Basic concepts of neural networks
- Common neural network structures
- Training and optimization methods
Topic 9: Natural Language Processing Lab Guide10%- NLP model training and evaluation
- Text preprocessing and feature engineering
- End-to-end application development

Huawei HCIP-AI-EI Developer V2.5 Sample Questions:

1. Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?

A) Color image input is multi-channel, whereas text input is single-channel.
B) For CNN, there is no difference in handling text or image tasks.
C) When the CNN is used for text tasks, the kernel size must be the same as the number of word vector dimensions. This constraint, however, does not apply to image tasks.
D) CNNs are suitable for image tasks, but they perform poorly in text tasks.


2. Which of the following applications are supported by ModelArts ExeML?

A) Automatic offering classification
B) Anomalous sound detection in production or security scenarios
C) Predictive maintenance of manufacturing equipment
D) Dress code conformance monitoring in campuses


3. The mAP evaluation metric in object detection combines accuracy and recall.

A) TRUE
B) FALSE


4. 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) Normalize the attention scores to obtain attention weights.
B) Compute the weighted sum of the value vectors using the attention weights.
C) Apply a non-linear mapping to the result obtained after the weighted summation.
D) Calculate the dot product similarity between the query and key vectors to obtain attention scores.


5. In an HSV color space, H is for hue, S is for saturation, and V is for value. Which of the following statements about the HSV color space are true?

A) Hue indicates the basic color attributes, such as red, green, and blue.
B) The HSV color space perceives colors differently from human eyes, so it is not suitable for image segmentation or color analysis.
C) Saturation describes how vivid the color is. The lower the saturation, the closer the color is to gray. The higher the saturation, the more vivid the color.
D) Value is a measure of brightness. The image brightness can be enhanced by processing the V component of the HSV color space.


Solutions:

Question # 1
Answer: A,C
Question # 2
Answer: A,B,C,D
Question # 3
Answer: B
Question # 4
Answer: A,B,D
Question # 5
Answer: A,C,D

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