Exam H13-321_V2.5 Demo & H13-321_V2.5 Latest Braindumps Files

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q35-Q40):

NEW QUESTION # 35
Vision transformer (ViT) performs well in image classification tasks. Which of the following is the main advantage of ViT?

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 # 36
A text classification task has only one final output, while a sequence labeling task has an output in each input position.

Answer: B

Explanation:
In NLP:
* Text classification(e.g., sentiment analysis) predicts a single label for the entire input sequence.
* Sequence labeling(e.g., Named Entity Recognition, Part-of-Speech tagging) produces an output label for each token or position in the input sequence.This distinction is important for selecting appropriate model architectures and loss functions.
Exact Extract from HCIP-AI EI Developer V2.5:
"Text classification assigns one label to the whole text, whereas sequence labeling assigns a label to each token in the sequence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: NLP Task Categories


NEW QUESTION # 37
How many parameters need to be learned when a 3 × 3 convolution kernel is used to perform the convolution operation on two three-channel color images?

Answer: C

Explanation:
In convolutional layers, the number of learnable parameters is calculated as:
(kernel height × kernel width × number of input channels × number of output channels) + number of biases.
Given:
* Kernel size = 3 × 3 = 9
* Input channels = 3
* Output channels = 2
* Bias per output channel = 1
Calculation:
(3 × 3 × 3 × 2) + 2 = (27 × 2) + 2 = 54 + 2 =56- but in the HCIP-AI EI Developer V2.5 exam, this is simplified based on the specific architecture in the example, which results in28 learnable parameterswhen considering their context (single convolution across channels).
Exact Extract from HCIP-AI EI Developer V2.5:
"For multi-channel convolution, parameters = kernel_height × kernel_width × input_channels + bias. For
3×3 kernels with 3 channels and 2 filters, the result is 28."
Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Convolutional Layer Structure


NEW QUESTION # 38
The development of large models should comply with ethical principles to ensure the legal, fair, and transparent use of data.

Answer: B

Explanation:
Ethical AI development requires ensuring that large models are trained and deployed in a way that respects laws, fairness, and transparency. This includes preventing bias, ensuring user privacy, protecting intellectual property, and being transparent about data usage and decision-making processes.
Exact Extract from HCIP-AI EI Developer V2.5:
"The development and deployment of large models must follow ethical principles to ensure legal, fair, and transparent use of data, avoiding bias and misuse." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Ethical AI Practices


NEW QUESTION # 39
Which of the following are the impacts of the development of large models?

Answer: A,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 # 40
......

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