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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q27-Q32):

NEW QUESTION # 27
Maximum likelihood estimation (MLE) requires knowledge of the sample data's distribution type.

A. FALSE
B. TRUE

Answer: B

Explanation:
Maximum likelihood estimation is a statistical method for estimating parameters of a probability distribution by maximizing the likelihood function. To apply MLE, theform of the probability distribution(e.g., normal, exponential) must be known in advance because the likelihood function is defined based on this distribution.
Without knowing the distribution type, the estimation process cannot be properly formulated.
Exact Extract from HCIP-AI EI Developer V2.5:
"MLE assumes that the underlying probability distribution type of the sample data is known and uses it to construct the likelihood function for parameter estimation." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Statistical Parameter Estimation

NEW QUESTION # 28
In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. The Transformer consists of an encoder and a(n) --------. (Fill in the blank.)

Answer:

Explanation:
Decoder
Explanation:
The Transformer model architecture includes:
* Encoder:Encodes the input sequence into contextualized representations.
* Decoder:Uses the encoder output and self-attention over previously generated tokens to produce the target sequence.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Transformer consists of an encoder-decoder structure, with self-attention mechanisms in both components for sequence-to-sequence learning." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Overview

NEW QUESTION # 29
Which of the following statements about the multi-head attention mechanism of the Transformer are true?

A. The multi-head attention mechanism captures information about different subspaces within a sequence.
B. Each header's query, key, and value undergo a shared linear transformation to obtain them.
C. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
D. The concatenated output is fed directly into the multi-headed attention mechanism.

Answer: A,C

NEW QUESTION # 30
In the image recognition algorithm, the structure design of the convolutional layer has a great impact on its performance. Which of the following statements are true about the structure and mechanism of the convolutional layer? (Transposed convolution is not considered.)

A. A stride in the convolutional layer can control the spatial resolution of the output feature map. A larger stride indicates a smaller output feature map and simpler calculation.
B. The convolutional layer slides over the input feature map using a convolution kernel of a fixed size to extract local features without explicitly defining their features.
C. In the convolutional layer, each neuron only collects some information. This effectively reduces the memory required.
D. The convolutional layer uses parameter sharing so that features at different positions share the same group of parameters. This reduces the number of network parameters required but reduces the expression capabilities of models.

Answer: A,B,C,D

Explanation:
The convolutional layer in CNNs is optimized for spatial feature extraction:
* Local connectivity(A) reduces computation and memory usage.
* Parameter sharing(B) reduces the number of learnable parameters and helps prevent overfitting.
* Stride control(C) allows adjusting the output resolution and computational cost.
* Sliding kernel operation(D) extracts local patterns without manual feature definition.
Exact Extract from HCIP-AI EI Developer V2.5:
"CNN convolutional layers leverage local connectivity, parameter sharing, and stride control to efficiently extract local features, reducing computational requirements compared to fully-connected layers." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Convolutional Neural Networks

NEW QUESTION # 31
Among image preprocessing techniques, gamma correction is a common non-linear brightness adjustment method. Which of the following statements are true about the application and features of gamma correction?

A. When # > 1, the input low grayscale range is compressed, and the high grayscale range is stretched, enhancing the bright areas while compressing the dark areas.
B. Gamma correction is an enhancement technique based on exponential transformation mapping. It is used for non-linear contrast stretching.
C. Gamma correction applies only to grayscale images and does not apply to color images.
D. When # < 1, the input high grayscale range is compressed, and the low grayscale range is stretched, enhancing the dark areas while compressing the bright areas.

Answer: A,B,D

Explanation:
Gamma correction is anon-linearimage processing method used to adjust brightness and contrast. It is not limited to grayscale images - it can be applied to both grayscale and color images by operating on individual channels.
* # < 1:Enhances dark regions (brightens shadows) and compresses highlights.
* # > 1:Enhances bright regions and compresses dark regions.It is based onpower-law (exponential) transformation, making it effective for adjusting human-perceived luminance.
Exact Extract from HCIP-AI EI Developer V2.5:
"Gamma correction is a non-linear brightness adjustment based on power-law transformation. It applies to both grayscale and color images. For #<1, dark regions are brightened; for #>1, bright regions are enhanced." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Enhancement

NEW QUESTION # 32
......

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H13-321_V2.5 Test Online, H13-321_V2.5 Cost Effective Dumps

Valid H13-321_V2.5 vce files, H13-321_V2.5 dumps latest

Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q27-Q32):

2025 H13-321_V2.5: HCIP-AI-EI Developer V2.5 Updated Test Online

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