H13-321_V2.5過去問 & H13-321_V2.5参考書勉強当社JPTestKingは、常にH13-321_V2.5認定の傾向を追ってきました。当社の研究開発チームは、H13-321_V2.5試験で出題される質問を調査するだけではありません。 H13-321_V2.5練習資料の内容は、試験のすべての質問が含まれるように慎重に選択されています。そして、私たちの教材には、いつでも、どこでも、読む、HCIP-AI-EI Developer V2.5テストする、勉強するのに役立つ3つの形式があります。つまり、当社の製品を使用すると、試験の準備を効率的に行うことができます。 H13-321_V2.5認定を希望される場合、当社Huaweiの製品が最適です。 Huawei HCIP-AI-EI Developer V2.5 認定 H13-321_V2.5 試験問題 (Q25-Q30):質問 # 25
Mel-frequency cepstral coefficients (MFCCs) take into account human auditory characteristics by first mapping the linear spectrum to the Mel nonlinear spectrum based on auditory perception, and then converting it to the cepstral domain.
A. FALSE
B. TRUE
正解：B
解説：
MFCCs are a widely used feature extraction method in speech recognition. The process involves:
* Converting the time-domain signal to the frequency domain using the Fourier transform.
* Mapping the frequency scale to theMel scaleto mimic human hearing perception.
* Taking the logarithm of the power spectrum to emphasize perceptually important differences.
* Applying the discrete cosine transform (DCT) to obtaincepstral coefficients.
These steps capture the spectral envelope, which is important for distinguishing phonemes in speech.
Exact Extract from HCIP-AI EI Developer V2.5:
"MFCCs transform audio to the Mel scale, applying log compression and cepstral transformation to align with human auditory characteristics." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Speech Feature Extraction
質問 # 26
Which of the following statements about the levels of natural language understanding are true?
A. Pragmatic analysis is to study the influence of the language's external environment on the language users.
B. Syntactic analysis is to find out the meaning of words, structural meaning, their combined meaning, so as to determine the true meaning or concept expressed by a language.
C. Speech analysis involves distinguishing independent phonemes from a speech stream based on phoneme rules, and then identifying syllables and their lexemes or words according to the phoneme form rules.
D. Semantic analysis is to analyze the structure of sentences and phrases to find out the relationship between words and phrases, as well as their functions in sentences.
E. Lexical analysis is to find the lexemes of a word and obtain linguistic information from them.
正解：A、C、E
解説：
* A:Incorrect - description given matches semantic analysis, not syntactic analysis.
* B:Incorrect - description given matches syntactic analysis, not semantic analysis.
* C:Correct - speech analysis focuses on phoneme recognition and word identification.
* D:Correct - lexical analysis identifies lexemes and retrieves their linguistic details.
* E:Correct - pragmatic analysis studies language use in context and environment.
Exact Extract from HCIP-AI EI Developer V2.5:
"Natural language understanding involves lexical, syntactic, semantic, speech, and pragmatic analyses, each focusing on different layers of language processing." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Levels of Language Understanding
質問 # 27
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. Compute the weighted sum of the value vectors using the attention weights.
B. Normalize the attention scores to obtain attention weights.
C. Calculate the dot product similarity between the query and key vectors to obtain attention scores.
D. Apply a non-linear mapping to the result obtained after the weighted summation.
正解：A、B、C
解説：
The standardattention mechanisminvolves:
* Computing attention scores via the dot product of query and key vectors (A).
* Applying a normalization function (typically softmax) to obtain attention weights (D).
* Using these weights to compute a weighted sum of the value vectors (B).OptionCis not a standard step
- non-linear mappings are not applied after the weighted sum in the basic attention formula.
Exact Extract from HCIP-AI EI Developer V2.5:
"Attention computes dot products between query and key, normalizes scores with softmax, and uses them to weight value vectors." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Attention Mechanism Fundamentals
質問 # 28
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.) 正解：
解説：
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
質問 # 29
In an HSV color space, H is for hue, S is for saturation, and V is for value. Which of the following statements about the HSV color space are true?
A. Hue indicates the basic color attributes, such as red, green, and blue.
B. Value is a measure of brightness. The image brightness can be enhanced by processing the V component of the HSV color space.
C. Saturation describes how vivid the color is. The lower the saturation, the closer the color is to gray. The higher the saturation, the more vivid the color.
D. The HSV color space perceives colors differently from human eyes, so it is not suitable for image segmentation or color analysis.
正解：A、B、C
解説：
The HSV model separates chromatic content (Hue, Saturation) from brightness (Value):
* H (Hue)efines the type of color (e.g., red, blue).
* S (Saturation):Measures vividness - low S means muted colors, high S means vivid colors.
* V (Value):Controls brightness - increasing V brightens the image.Contrary to option D, HSV aligns more closely with human perception than RGB, making itsuitablefor segmentation and color-based analysis.
Exact Extract from HCIP-AI EI Developer V2.5:
"HSV separates hue, saturation, and brightness, making it closer to human vision perception and suitable for color-based image analysis." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Color Spaces
efines the type of color (e.g., red, blue).