H13-321_V2.5 by Huawei Actual Free Exam Q&As

Question 1

Seq2Seq is a model that translates one sequence into another sequence, essentially consisting of two recurrent neural networks (RNNs), one is the Encoder, and the other is the ---------. (Fill in the blank.)

Discussion 0

Correct Answer:

Decoder
Explanation:
The Seq2Seq architecture is widely used in machine translation, speech recognition, and other NLP tasks. It consists of:
* Encoder:Processes the input sequence and encodes it into a fixed-length context vector containing semantic information.
* Decoder:Uses this context vector to generate the target output sequence step by step.
Exact Extract from HCIP-AI EI Developer V2.5:
"Seq2Seq models consist of an encoder and a decoder. The encoder transforms the input into a context vector, which the decoder uses to generate the output sequence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Encoder-Decoder Architecture

Question 2

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

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Correct Answer: A,C,D Vote an answer

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Question 3

The deep neural network (DNN)-hidden Markov model (HMM) does not require the HMM-Gaussian mixture model (GMM) as an auxiliary.

A. TRUE B. FALSE

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Correct Answer: B Vote an answer

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Question 4

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

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Correct Answer: A,B,C Vote an answer

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Question 5

If a scanned document is not properly placed, and the text is tilted, it is difficult to recognize the characters in the document. Which of the following techniques can be used for correction in this case?

A. Affine transformation B. Grayscale transformation C. Perspective transformation D. Rotational transformation

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Correct Answer: A,D Vote an answer

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Question 6

When the chi-square test is used for feature selection, SelectKBest and _____ function or class must be imported from the sklearn.feature_selection module. (Enter the function interface name.) chi2 Explanation:
In feature selection for classification tasks, thechi-square (#²)statistical test can be applied to evaluate the independence between features and target labels.
In Python's scikit-learn library, this is implemented using:

Discussion 0

Correct Answer:

python
CopyEdit
from sklearn.feature_selection import SelectKBest, chi2
SelectKBest selects the top K features based on scores returned by the chi2 function.
Exact Extract from HCIP-AI EI Developer V2.5:
"In scikit-learn, SelectKBest with chi2 can be used for feature selection by scoring features according to the chi-square statistic." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Feature Selection Methods

Huawei HCIP-AI-EI Developer V2.5 - H13-321_V2.5 Exam Practice Test