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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q59-Q64):

NEW QUESTION # 59
The deep neural network (DNN)-hidden Markov model (HMM) does not require the HMM-Gaussian mixture model (GMM) as an auxiliary.

Answer: B

Explanation:
In traditional hybridDNN-HMMspeech recognition systems, the DNN is often trained usingframe-level alignmentsgenerated by anHMM-GMMsystem. The GMM serves as an auxiliary tool to perform initial alignments between audio frames and phonetic units, which are then used to train the DNN. Without the HMM-GMM step, supervised training of the DNN in this context is typically not possible.
Exact Extract from HCIP-AI EI Developer V2.5:
"In a DNN-HMM hybrid system, the DNN replaces the GMM in modeling emission probabilities, but GMMs are still used in the initial alignment process to prepare training data for the DNN." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Hybrid Speech Recognition Models


NEW QUESTION # 60
Which of the following is not an acoustic feature of speech?

Answer: A

Explanation:
In speech signal processing,acoustic featuresdescribe measurable physical properties of sound waves, such as duration(time length),frequency(pitch), andamplitude(loudness). These features are used in speech recognition and speaker identification systems.
Semantics, on the other hand, refers to the meaning of speech - a linguistic attribute, not an acoustic property. Therefore, it is not classified as an acoustic feature.
Exact Extract from HCIP-AI EI Developer V2.5:
"Speech features include duration, frequency, and amplitude. These are acoustic characteristics, distinct from semantic information, which relates to language meaning." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Speech Feature Extraction


NEW QUESTION # 61
The U-Net uses an upsampling mechanism and has a fully-connected layer.

Answer: B

Explanation:
U-Net is a convolutional neural network architecture designed for biomedical image segmentation. It consists of a contracting path for feature extraction and an expansive path for precise localization, usingupsamplingin the decoding path. However, U-Netdoes not include fully-connected layers; instead, it uses only convolutional layers to maintain spatial information. Removing fully-connected layers ensures the network can handle images of varying sizes without requiring fixed input dimensions.
Exact Extract from HCIP-AI EI Developer V2.5:
"U-Net architecture is fully convolutional and avoids fully-connected layers to preserve spatial resolution, relying on upsampling in the decoder path for segmentation tasks." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Semantic Segmentation Networks


NEW QUESTION # 62
Which of the following statements about the levels of natural language understanding are true?

Answer: B,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 # 63
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?

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 # 64
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