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Test Huawei H13-321_V2.5 Collection Pdf, New H13-321_V2.5 Test Cram

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Test Huawei H13-321_V2.5 Collection Pdf, New H13-321_V2.5 Test Cram

Posted at yesterday 09:42      View：3 | Replies：0        Print      Only Author   [Copy Link] 1#
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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q20-Q25):NEW QUESTION # 20
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).
  • A. FALSE
  • B. TRUE
Answer: B
Explanation:
A Gaussian mixture model represents a probability distribution as a weighted sum of multiple Gaussian components. TheMLEmethod can be applied to estimate the parameters of these components (means, variances, and mixing coefficients) by maximizing the likelihood of the observed data. The Expectation- Maximization (EM) algorithm is typically used to perform MLE in GMMs because it can handle hidden (latent) variables representing the component assignments.
Exact Extract from HCIP-AI EI Developer V2.5:
"MLE, implemented through the EM algorithm, is commonly used to estimate the parameters of Gaussian mixture models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Gaussian Mixture Models

NEW QUESTION # 21
Which of the following has never been used as a method in the history of NLP?
  • A. Statistics-based method
  • B. Deep learning-based method
  • C. Rule-based method
  • D. Recursion-based method
Answer: D
Explanation:
Historically, NLP has evolved through three main methodological phases:
* Rule-based methods- used in early systems, relying on manually crafted grammar and lexicons.
* Statistics-based methods- introduced probabilistic models such as HMMs and n-grams.
* Deep learning-based methods- using neural networks, transformers, and embeddings.
A "recursion-based method" has never been recognized as a distinct NLP methodology, even though recursion can appear in linguistic theory, it is not a primary computational approach in NLP history.
Exact Extract from HCIP-AI EI Developer V2.5:
"The evolution of NLP includes rule-based, statistical, and deep learning-based methods. Recursion-based approaches are not considered a formal method in NLP development history." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: NLP Development History

NEW QUESTION # 22
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. FALSE
  • B. TRUE
Answer: A
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 # 23
When training a deep neural network model, a loss function measures the difference between the model's predictions and the actual labels.
  • A. FALSE
  • B. TRUE
Answer: B
Explanation:
In the HCIP-AI EI Developer V2.5 study guide, the loss function is defined as a core component in training deep neural network models. It serves as a quantitative measure of how well the model's predictions match the actual ground truth labels. By calculating the difference between predicted outputs and actual labels, the loss function provides feedback that the optimization algorithm (such as gradient descent) uses to update model parameters. This process is iterative, aiming to minimize the loss value, thereby improving prediction accuracy. For example, in classification tasks,Cross-Entropy Lossis commonly used, while in regression tasks,Mean Squared Error (MSE)is typical. The smaller the loss, the better the model's performance on the given data.
Exact Extract from HCIP-AI EI Developer V2.5:
"A loss function is an objective function that evaluates the difference between the model output and the real label. The goal of training is to minimize this loss so that the model predictions approach the actual values." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Model Training and Evaluation

NEW QUESTION # 24
Which of the following is not an algorithm for training word vectors?
  • A. FastText
  • B. Word2Vec
  • C. TextCNN
  • D. BERT
Answer: C
Explanation:
* Word2VecandFastTextare neural network-based algorithms designed for generating dense vector representations of words.
* BERTis a transformer-based language model that also generates contextualized word embeddings.
* TextCNN, however, is a text classification model, not a word vector training algorithm. It uses convolutional neural networks to extract features from already vectorized text but does not learn static word embeddings in the same sense as Word2Vec or FastText.
Exact Extract from HCIP-AI EI Developer V2.5:
"Word2Vec, FastText, and BERT can be used to train word embeddings. TextCNN is a classification model that uses embeddings but does not train them as its primary function." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Word Vector Representation

NEW QUESTION # 25
......
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