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[General] Free PDF Quiz 2026 H13-321_V2.5: Updated HCIP-AI-EI Developer V2.5 Exam Exercise

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【General】 Free PDF Quiz 2026 H13-321_V2.5: Updated HCIP-AI-EI Developer V2.5 Exam Exercise

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q50-Q55):NEW QUESTION # 50
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 # 51
The mAP evaluation metric in object detection combines accuracy and recall.
  • A. TRUE
  • B. FALSE
Answer: B
Explanation:
ThemAP(mean Average Precision) metric in object detection combinesprecisionandrecall, not accuracy and recall. mAP is calculated by averaging the Average Precision (AP) across all classes in a dataset. Precision measures how many predicted positives are correct, while recall measures how many actual positives are identified. Accuracy, on the other hand, is a general metric for classification tasks and is less suitable for object detection where class imbalance and localization are important.
Exact Extract from HCIP-AI EI Developer V2.5:
"mAP evaluates object detection performance by considering both precision and recall across all classes, providing a balanced measure of detection accuracy and completeness." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection Metrics

NEW QUESTION # 52
Which of the following statements about the functions of layer normalization and residual connection in the Transformer is true?
  • A. Layer normalization accelerates model convergence and does not affect model stability.
  • B. Residual connections primarily add depth to the model but do not aid in gradient propagation.
  • C. Residual connections and layer normalization help prevent vanishing gradients and exploding gradients in deep networks.
  • D. In shallow networks, residual connections are beneficial, but they aggravate the vanishing gradient problem in deep networks.
Answer: C
Explanation:
In Transformers:
* Residual connectionshelp preserve gradient flow through deep networks, mitigating vanishing
/exploding gradient issues.
* Layer normalizationstabilizes training by normalizing across features, improving convergence speed and training stability.Thus,Ais correct, while B, C, and D are incorrect.
Exact Extract from HCIP-AI EI Developer V2.5:
"Residual connections and layer normalization stabilize deep network training, prevent gradient issues, and accelerate convergence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Training Mechanisms

NEW QUESTION # 53
In the field of deep learning, which of the following activation functions has a derivative not greater than 0.5?
  • A. Tanh
  • B. Sigmoid
  • C. SeLU
  • D. ReLU
Answer: B
Explanation:
Thesigmoidactivation function maps inputs to the range (0, 1) and has a maximum derivative of 0.25 at x=0.
This derivative value is always # 0.5, making it the correct choice here. While sigmoid is historically used in neural networks, it suffers from the vanishing gradient problem for large positive or negative inputs due to its small derivative values. Other functions such as ReLU, Tanh, and SeLU have different derivative behaviors, with ReLU having a derivative of 1 for positive inputs, Tanh having derivatives up to 1, and SeLU designed for self-normalizing networks with derivatives potentially greater than 0.5.
Exact Extract from HCIP-AI EI Developer V2.5:
"Sigmoid compresses values into the (0,1) range, with its maximum derivative being 0.25, which is always less than 0.5." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Activation Functions in Neural Networks

NEW QUESTION # 54
------- is a model that uses a convolutional neural network (CNN) to classify texts.
Answer:
Explanation:
Text CNN
Explanation:
Text CNN applies convolutional layers directly to text data represented as word embeddings. By using multiple kernel sizes, Text CNN captures features from n-grams of varying lengths. These features are pooled and passed to fully connected layers for classification tasks such as sentiment analysis or spam detection.
Exact Extract from HCIP-AI EI Developer V2.5:
"Text CNN applies convolution and pooling over word embeddings to extract local features for text classification." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: CNN Applications in NLP

NEW QUESTION # 55
......
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