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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q26-Q31):

NEW QUESTION # 26
Which audio file formats can Huawei Cloud text-to-speech (TTS) generate?

A. AAC
B. MP3
C. WAV
D. PCM

Answer: B,C,D

Explanation:
Huawei Cloud's TTS service supports generating synthesized speech in multiple formats to meet different application requirements:
* WAV:High-quality, uncompressed audio format.
* MP3:Compressed format for efficient storage and streaming.
* PCM:Raw, uncompressed audio data for low-level processing.
AAC is a common format but isnotsupported by Huawei Cloud TTS as per the official specification for HCIP- AI EI Developer V2.5.
Exact Extract from HCIP-AI EI Developer V2.5:
"Huawei Cloud TTS supports output in WAV, MP3, and PCM formats to balance audio quality and storage requirements." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Text-to-Speech Service

NEW QUESTION # 27
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

Answer: B

Explanation:
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

NEW QUESTION # 28
Which of the following statements about the levels of natural language understanding are true?

A. 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.
B. 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.
C. Lexical analysis is to find the lexemes of a word and obtain linguistic information from them.
D. 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.
E. Pragmatic analysis is to study the influence of the language's external environment on the language users.

Answer: C,D,E

Explanation:
* 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

NEW QUESTION # 29
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. 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).
B. Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.
C. Transformer models outperform RNN and CNN in processing long texts because they can effectively capture global dependencies.
D. 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.

Answer: A,C,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 # 30
The development of large models should comply with ethical principles to ensure the legal, fair, and transparent use of data.

A. FALSE
B. TRUE

Answer: B

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 # 31
......

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Valid Flexible H13-321_V2.5 Learning Mode | 100% Free Exam H13-321_V2.5 Vce Format

2025 Pass-Sure H13-321_V2.5: Flexible HCIP-AI-EI Developer V2.5 Learning Mode

Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q26-Q31):

2025 H13-321_V2.5: Marvelous Flexible HCIP-AI-EI Developer V2.5 Learning Mode

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