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

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

A) Residual connections primarily add depth to the model but do not aid in gradient propagation.
B) Layer normalization accelerates model convergence and does not affect model stability.
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.

2. Which of the following has never been used as a method in the history of NLP?

A) Deep learning-based method
B) Statistics-based method
C) Recursion-based method
D) Rule-based method

3. 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) 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.
C) Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
D) 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.

4. In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. The Transformer consists of an encoder and a(n) --------. (Fill in the blank.)

5. 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) TRUE
B) FALSE

Solutions: