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Quiz Huawei - Marvelous H13-321_V2.5 - HCIP-AI-EI Developer V2.5 Study Materials

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q15-Q20):

NEW QUESTION # 15
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. TRUE
B. FALSE

Answer: B

Explanation:
TheTransformerarchitecture introduced in 2017 eliminates recurrence (RNN) and convolution entirely, relying solely on self-attention mechanisms and feed-forward layers. It does not contain LSTM or CNN components, which distinguishes it from previous sequence models.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Transformer architecture does not use RNNs or CNNs. It relies entirely on self-attention and feed- forward networks for sequence modeling." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Architecture Overview

NEW QUESTION # 16
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. 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).
C. Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.
D. Transformer models outperform RNN and CNN in processing long texts because they can effectively capture global dependencies.

Answer: A,B,D

Explanation:
Transformers are designed for sequence modeling without recurrence or convolution.
* A:True - self-attention captures global dependencies efficiently, outperforming RNNs/CNNs in long text processing.
* B:True - multi-head attention computes multiple attention projections in parallel.
* C:True - the architecture is purely attention-based.
* D:False - positional encoding isrequiredbecause self-attention does not inherently encode sequence order.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Transformer uses self-attention to model dependencies and multi-head attention to capture features in different subspaces. Positional encoding must be added to preserve sequence order." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Architecture

NEW QUESTION # 17
Which of the following statements about the functions of layer normalization and residual connection in the Transformer is true?

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

Answer: A

Explanation:
In Transformers:
* Residual connectionshelp preserve gradient flow through deep networks, mitigating vanishing
/exploding gradient issues.
* Layer normalizationstabilizes training by normalizing across features, improving convergence speed and training stability.Thus,Ais correct, while B, C, and D are incorrect.
Exact Extract from HCIP-AI EI Developer V2.5:
"Residual connections and layer normalization stabilize deep network training, prevent gradient issues, and accelerate convergence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Training Mechanisms

NEW QUESTION # 18
The development of large models should comply with ethical principles to ensure the legal, fair, and transparent use of data.

A. TRUE
B. FALSE

Answer: A

Explanation:
Ethical AI development requires ensuring that large models are trained and deployed in a way that respects laws, fairness, and transparency. This includes preventing bias, ensuring user privacy, protecting intellectual property, and being transparent about data usage and decision-making processes.
Exact Extract from HCIP-AI EI Developer V2.5:
"The development and deployment of large models must follow ethical principles to ensure legal, fair, and transparent use of data, avoiding bias and misuse." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Ethical AI Practices

NEW QUESTION # 19
When training a deep neural network model, a loss function measures the difference between the model's predictions and the actual labels.

A. TRUE
B. FALSE

Answer: A

Explanation:
In the HCIP-AI EI Developer V2.5 study guide, the loss function is defined as a core component in training deep neural network models. It serves as a quantitative measure of how well the model's predictions match the actual ground truth labels. By calculating the difference between predicted outputs and actual labels, the loss function provides feedback that the optimization algorithm (such as gradient descent) uses to update model parameters. This process is iterative, aiming to minimize the loss value, thereby improving prediction accuracy. For example, in classification tasks,Cross-Entropy Lossis commonly used, while in regression tasks,Mean Squared Error (MSE)is typical. The smaller the loss, the better the model's performance on the given data.
Exact Extract from HCIP-AI EI Developer V2.5:
"A loss function is an objective function that evaluates the difference between the model output and the real label. The goal of training is to minimize this loss so that the model predictions approach the actual values." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Model Training and Evaluation

NEW QUESTION # 20
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