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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 methods are useful when tackling overfitting?

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

2. Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).

A) FALSE
B) TRUE

3. In the field of deep learning, which of the following activation functions has a derivative not greater than 0.5?

A) ReLU
B) Sigmoid
C) SeLU
D) Tanh

4. In cases where the bright and dark areas of an image are too extreme, which of the following techniques can be used to improve the image?

A) Inversion
B) Grayscale stretching
C) Gamma correction
D) Grayscale compression

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

Solutions: