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[General] Reliable Huawei H13-321_V2.5 Test Experience | H13-321_V2.5 Exam Simulator Fee

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【General】 Reliable Huawei H13-321_V2.5 Test Experience | H13-321_V2.5 Exam Simulator Fee

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q54-Q59):NEW QUESTION # 54
Which of the following is a learning algorithm used for Markov chains?
  • A. Forward-backward algorithm
  • B. Baum-Welch algorithm
  • C. Viterbi algorithm
  • D. Exhaustive search
Answer: B
Explanation:
TheBaum-Welch algorithmis a special case of the Expectation-Maximization (EM) algorithm used to train Hidden Markov Models (HMMs). It estimates model parameters (transition probabilities, emission probabilities) when the training data is incomplete or hidden.
* Viterbi algorithmis for decoding, not training.
* Forward-backward algorithmis part of Baum-Welch's expectation step but is not a standalone training method.
* Exhaustive searchis not a standard HMM training algorithm.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Baum-Welch algorithm iteratively optimizes HMM parameters using forward and backward probability computations until convergence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: HMM Training Algorithms

NEW QUESTION # 55
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 # 56
In the deep neural network (DNN)-hidden Markov model (HMM), the DNN is mainly used for feature processing, while the HMM is mainly used for sequence modeling.
  • A. FALSE
  • B. TRUE
Answer: B
Explanation:
In hybridDNN-HMMspeech recognition:
* TheDNNacts as an acoustic model, transforming audio features into probability estimates for phonetic states.
* TheHMMmodels the temporal sequence and transitions between phonetic states, handling time dependencies and variability in speech.
This combination leverages the representational power of DNNs and the sequence modeling strengths of HMMs.
Exact Extract from HCIP-AI EI Developer V2.5:
"In DNN-HMM systems, the DNN outputs state posterior probabilities, and the HMM models the temporal sequence structure of speech." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Hybrid Speech Recognition Models

NEW QUESTION # 57
What type of task is viewed when using the Seq2Seq model in speech recognition?
  • A. Classification task
  • B. Clustering task
  • C. Dimensionality reduction task
  • D. Regression task
Answer: A
Explanation:
The Seq2Seq (sequence-to-sequence) model converts an input sequence into an output sequence. In speech recognition, the input is a sequence of acoustic features, and the output is a sequence of text tokens. This is essentially aclassification taskbecause each output token is classified into a predefined vocabulary set.
Although the output is sequential, each position in the output sequence involves a classification decision.
Exact Extract from HCIP-AI EI Developer V2.5:
"In speech recognition, Seq2Seq models classify each output token from a fixed vocabulary, making the overall problem a sequence of classification tasks." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Sequence Models in Speech Recognition

NEW QUESTION # 58
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 # 59
......
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