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Huawei H13-321_V2.5 Exam Syllabus Topics:

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

Question 1

Which of the following methods are useful when tackling overfitting?

A. Using dropout during model training
B. Using parameter norm penalties
C. Using more complex models
D. Data augmentation

Question 2

The natural language processing field usually uses distributed semantic representation to represent words.
Each word is no longer a completely orthogonal 0-1 vector, but a point in a multi-dimensional real number space, which is specifically represented as a real number vector.

A. FALSE
B. TRUE

Question 3

Which of the following statements about the functions of layer normalization and residual connection in the Transformer is true?

A. Layer normalization accelerates model convergence and does not affect model stability.
B. Residual connections primarily add depth to the model but do not aid in gradient propagation.
C. In shallow networks, residual connections are beneficial, but they aggravate the vanishing gradient problem in deep networks.
D. Residual connections and layer normalization help prevent vanishing gradients and exploding gradients in deep networks.

Question 4

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. FALSE
B. TRUE

Question 5

In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. In a Transformer model, there is customized LSTM with CNN layers.

A. FALSE
B. TRUE

Solutions: