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[General] Huawei H13-321_V2.5 Reliable Exam Cram | H13-321_V2.5 Pdf Files

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【General】 Huawei H13-321_V2.5 Reliable Exam Cram | H13-321_V2.5 Pdf Files

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q31-Q36):NEW QUESTION # 31
The accuracy of object location detection can be evaluated using the intersection over union (IoU) value, which is a ratio. The denominator is the overlapping area between the prediction bounding box and ground truth bounding box, and the numerator is the area of union encompassed by both boxes.
  • A. TRUE
  • B. FALSE
Answer: B
Explanation:
TheIoUmetric is defined as:
IoU = (Area of Overlap) / (Area of Union)
* Numerator:Area of overlap between the predicted bounding box and the ground truth bounding box.
* Denominator:Area of union of both bounding boxes.
The statement given in the questionreversesthe numerator and denominator, which is why it is incorrect. IoU is crucial for object detection evaluation, and higher IoU values indicate better localization accuracy.
Exact Extract from HCIP-AI EI Developer V2.5:
"Intersection over Union (IoU) is calculated as the ratio of the intersection area between prediction and ground truth bounding boxes to their union area." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection Metrics

NEW QUESTION # 32
What are the advantages of deep learning-based speech recognition algorithms?
  • A. End-to-end task processing
  • B. Automated feature extraction
  • C. No data training
  • D. Forced alignment of annotated data
Answer: A,B
Explanation:
Deep learning-based speech recognition offers two key advantages over traditional approaches:
* Automated feature extraction (B):Neural networks can directly learn features from raw or lightly processed audio without manual engineering of MFCCs or filter banks.
* End-to-end task processing (C):Models like CTC-based networks or attention-based architectures can map audio inputs directly to text outputs without intermediate models like GMM-HMM.
Options A and D are incorrect because forced alignment is part of traditional GMM-HMM systems, and deep learning still requires training with large datasets.
Exact Extract from HCIP-AI EI Developer V2.5:
"Deep learning models support automatic feature extraction and can implement end-to-end mapping from speech signals to text outputs." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: End-to-End Speech Recognition

NEW QUESTION # 33
Which of the following are the impacts of the development of large models?
  • A. Model pre-training costs will be reduced
  • B. The accuracy and efficiency of natural language processing tasks will improve
  • C. Large models will completely replace small and domain-specific models
  • D. Data privacy and security issues will be exacerbated
Answer: B,D
Explanation:
The emergence of large AI models (e.g., GPT, Pangu, BERT) has led to:
* C:Improved accuracy and efficiency in NLP and other AI tasks because of their ability to capture deep semantic and contextual information.
* D:Increased data privacy and security concerns, as large models require massive datasets which may contain sensitive or proprietary information.Ais false - large models increase pre-training costs.Bis false - small and domain-specific models still play important roles due to efficiency and deployment constraints.
Exact Extract from HCIP-AI EI Developer V2.5:
"Large models improve task performance but raise privacy and security concerns. They do not necessarily reduce training cost or eliminate the need for smaller models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Large Model Trends and Challenges

NEW QUESTION # 34
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 # 35
Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?
  • A. Color image input is multi-channel, whereas text input is single-channel.
  • B. CNNs are suitable for image tasks, but they perform poorly in text tasks.
  • C. For CNN, there is no difference in handling text or image tasks.
  • D. When the CNN is used for text tasks, the kernel size must be the same as the number of word vector dimensions. This constraint, however, does not apply to image tasks.
Answer: A,D
Explanation:
In CNN usage:
* A:True - color images have multiple channels (e.g., RGB = 3), while text inputs are represented as sequences of word embeddings, typically single-channel in structure.
* B:True - in text tasks, the convolution kernel height must match the embedding dimension to capture complete token information, which is not a constraint in images.
* C:False - there are clear differences in handling between text and image data.
* D:False - CNNs can perform very well in text classification when used appropriately.
Exact Extract from HCIP-AI EI Developer V2.5:
"In text CNNs, convolution kernels span the entire embedding dimension, whereas in image CNNs, kernel size is independent of channel count." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: CNN in NLP

NEW QUESTION # 36
......
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