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100% Pass Valid H13-321_V2.5 - HCIP-AI-EI Developer V2.5 High Quality

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q45-Q50):

NEW QUESTION # 45
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. Grayscale compression
B. Grayscale stretching
C. Gamma correction
D. Inversion

Answer: C

Explanation:
When the contrast between bright and dark areas is extreme,gamma correctionis effective in adjusting luminance in a non-linear way to balance these extremes.
* If# < 1, dark areas are brightened, highlights are compressed.
* If# > 1, bright areas are emphasized, shadows are compressed.Other methods like grayscale stretching and compression target linear contrast changes, while inversion flips pixel values but doesn't balance extreme light/dark ranges effectively.
Exact Extract from HCIP-AI EI Developer V2.5:
"Gamma correction adjusts image brightness non-linearly, suitable for correcting overly bright or overly dark regions, improving overall visibility." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Enhancement

NEW QUESTION # 46
The jieba ------() method can be used for word segmentation.

Answer:

Explanation:
cut
Explanation:
In Python'sjiebalibrary, the cut() method is used for Chinese word segmentation. It splits a given sentence into individual words based on probabilistic models and a dictionary. The method supports both precise mode and full mode, with precise mode being the default for balanced accuracy and completeness.
Exact Extract from HCIP-AI EI Developer V2.5:
"The jieba.cut() method segments Chinese text into words, supporting multiple modes for different application needs." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Chinese Word Segmentation Tools

NEW QUESTION # 47
Overfitting is a condition where a model is overly simple and excessive generalization errors occur.

A. FALSE
B. TRUE

Answer: A

Explanation:
Overfitting occurs when a model learns the training data too well, including its noise and outliers, to the extent that it negatively impacts performance on unseen data. Contrary to the statement, overfitting is not caused by an "overly simple" model but typically by an overlycomplex modelwith too many parameters relative to the amount of training data. Such models have high variance and low bias, meaning they fit the training data perfectly but fail to generalize to new datasets. In the HCIP-AI EI Developer V2.5 curriculum, overfitting is described as a scenario where the model's complexity captures random fluctuations in training data instead of general patterns, leading to poor predictive performance.
Exact Extract from HCIP-AI EI Developer V2.5:
"Overfitting means that the trained model performs very well on the training dataset but poorly on new data.
It usually results from excessive model complexity, insufficient data, or lack of regularization." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Model Training Challenges

NEW QUESTION # 48
The objective of -------- is to extract and classify named entities in a text into pre-defined classes such as names, organizations, locations, time expressions, monetary values, and percentages. (Enter the abbreviation.)

Answer:

Explanation:
NER
Explanation:
NER(Named Entity Recognition) is a core NLP task that involves locating and categorizing entities within text into predefined categories like persons, organizations, places, dates, monetary values, and percentages.
NER is widely used in information extraction, question answering, and knowledge graph construction.
Exact Extract from HCIP-AI EI Developer V2.5:
"NER identifies and classifies named entities in text into categories such as person names, organizations, locations, time expressions, and numeric values." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Sequence Labeling Tasks

NEW QUESTION # 49
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. Transformer models outperform RNN and CNN in processing long texts because they can effectively capture global dependencies.
D. Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.

Answer: A,B,C

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 # 50
......

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