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Huawei H13-321_V2.5 Exam Syllabus Topics:

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

Question 1

The U-Net uses an upsampling mechanism and has a fully-connected layer.

A. TRUE
B. FALSE

Question 2

In natural language processing tasks, word vector evaluation is an important aspect for measuring the performance of a word embedding model. Which of the following statements about word vector evaluation are true?

A. The word analogy task evaluates the capability of word vectors in capturing semantic relationships between words, for example, by determining whether "king - man + woman = ?" is close to "queen".
B. Extrinsic evaluation is the main method used for evaluating word vectors because it directly reflects the performance of word vectors in real-world application tasks.
C. Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
D. Word similarity tasks typically employ manually labeled datasets to evaluate word vectors, compute the cosine similarity between word vectors, and compare it with the manual labeling result.

Question 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

Question 4

Which of the following are required for the image object detection algorithm?

A. Object location calculation
B. Object contour calculation
C. Object classification determination
D. Confidence calculation

Question 5

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. Transformer models outperform RNN and CNN in processing long texts because they can effectively capture global dependencies.
B. 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.
C. Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.
D. 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).

Solutions: