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Huawei H13-321_V2.5 Exam Syllabus Topics:

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

1. Which of the following statements about the multi-head attention mechanism of the Transformer are true?

A) The multi-head attention mechanism captures information about different subspaces within a sequence.
B) The concatenated output is fed directly into the multi-headed attention mechanism.
C) The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
D) Each header's query, key, and value undergo a shared linear transformation to obtain them.

2. 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) Value is a measure of brightness. The image brightness can be enhanced by processing the V component of the HSV color space.
B) 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.
C) Hue indicates the basic color attributes, such as red, green, and blue.
D) The HSV color space perceives colors differently from human eyes, so it is not suitable for image segmentation or color analysis.

3. The accuracy of object location detection can be evaluated using the intersection over union (IoU) value, which is a ratio. The denominator is the overlapping area between the prediction bounding box and ground truth bounding box, and the numerator is the area of union encompassed by both boxes.

A) TRUE
B) FALSE

4. In the image recognition algorithm, the structure design of the convolutional layer has a great impact on its performance. Which of the following statements are true about the structure and mechanism of the convolutional layer? (Transposed convolution is not considered.)

A) In the convolutional layer, each neuron only collects some information. This effectively reduces the memory required.
B) The convolutional layer uses parameter sharing so that features at different positions share the same group of parameters. This reduces the number of network parameters required but reduces the expression capabilities of models.
C) A stride in the convolutional layer can control the spatial resolution of the output feature map. A larger stride indicates a smaller output feature map and simpler calculation.
D) The convolutional layer slides over the input feature map using a convolution kernel of a fixed size to extract local features without explicitly defining their features.

5. What type of task is viewed when using the Seq2Seq model in speech recognition?

A) Dimensionality reduction task
B) Regression task
C) Classification task
D) Clustering task

Solutions: