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[General] H13-321_V2.5 Instant Discount | H13-321_V2.5 Exam Success

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【General】 H13-321_V2.5 Instant Discount | H13-321_V2.5 Exam Success

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q17-Q22):NEW QUESTION # 17
Which of the following statements about the multi-head attention mechanism of the Transformer are true?
  • A. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
  • B. The concatenated output is fed directly into the multi-headed attention mechanism.
  • C. The multi-head attention mechanism captures information about different subspaces within a sequence.
  • D. Each header's query, key, and value undergo a shared linear transformation to obtain them.
Answer: A,C

NEW QUESTION # 18
Vision transformer (ViT) performs well in image classification tasks. Which of the following is the main advantage of ViT?
  • A. It achieves fast convergence without using pre-trained models.
  • B. It can handle small datasets with minimal labeling required.
  • C. It can process high-resolution images to enhance classification accuracy.
  • D. The self-attention mechanism is used to capture global features of images, improving classification accuracy.
Answer: D
Explanation:
TheVision Transformer (ViT)applies the transformer architecture to image patches. Its key advantage is the use ofself-attentionto capture global dependencies and relationships between all parts of an image. This allows ViT to excel in classification accuracy, especially on large datasets with sufficient pre-training.
Exact Extract from HCIP-AI EI Developer V2.5:
"ViT applies self-attention to image patches, enabling global feature extraction and improving classification performance compared to local receptive fields in CNNs." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Models in Vision

NEW QUESTION # 19
In the field of deep learning, which of the following activation functions has a derivative not greater than 0.5?
  • A. ReLU
  • B. SeLU
  • C. Sigmoid
  • D. Tanh
Answer: C
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 # 20
Which of the following applications are supported by ModelArts ExeML?
  • A. Dress code conformance monitoring in campuses
  • B. Automatic offering classification
  • C. Anomalous sound detection in production or security scenarios
  • D. Predictive maintenance of manufacturing equipment
Answer: A,B,C,D
Explanation:
ModelArtsExeML(Expert Experience Machine Learning) enables users without programming expertise to build AI models through a visual interface. It supports multiple application scenarios, including:
* Predictive maintenance in manufacturing to detect potential equipment failures.
* Monitoring compliance with dress codes in school or workplace settings.
* Detecting unusual sounds in manufacturing or security contexts.
* Classifying offerings automatically in e-commerce or retail systems.
Exact Extract from HCIP-AI EI Developer V2.5:
"ModelArts ExeML supports intelligent applications in industrial maintenance, campus security, sound anomaly detection, and automated product classification." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: ModelArts ExeML Application Scenarios

NEW QUESTION # 21
The basic operations of morphological processing include dilation and erosion. These operations can be combined to achieve practical algorithms such as opening and closing operations.
  • A. TRUE
  • B. FALSE
Answer: A
Explanation:
Morphological processing in image analysis is used to process binary or grayscale images based on shape.
* Dilation:Expands object boundaries, useful for filling small holes.
* Erosion:Shrinks object boundaries, useful for removing noise.By combining them:
* Opening:Erosion followed by dilation (removes small objects/noise).
* Closingilation followed by erosion (fills small holes).
Exact Extract from HCIP-AI EI Developer V2.5:
"Morphological processing is based on dilation and erosion. Opening and closing are composite operations derived from these two to handle noise removal and hole filling." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Morphological Image Processing

NEW QUESTION # 22
......
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