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H13-321_V2.5합격보장가능인증덤프완벽한덤프최신버전

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최신 HCIP-AI EI Developer H13-321_V2.5 무료샘플문제 (Q46-Q51):

질문 # 46
The U-Net uses an upsampling mechanism and has a fully-connected layer.

A. FALSE
B. TRUE

정답：A

설명：
U-Net is a convolutional neural network architecture designed for biomedical image segmentation. It consists of a contracting path for feature extraction and an expansive path for precise localization, usingupsamplingin the decoding path. However, U-Netdoes not include fully-connected layers; instead, it uses only convolutional layers to maintain spatial information. Removing fully-connected layers ensures the network can handle images of varying sizes without requiring fixed input dimensions.
Exact Extract from HCIP-AI EI Developer V2.5:
"U-Net architecture is fully convolutional and avoids fully-connected layers to preserve spatial resolution, relying on upsampling in the decoder path for segmentation tasks." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Semantic Segmentation Networks

질문 # 47
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).

A. FALSE
B. TRUE

정답：B

설명：
A Gaussian mixture model represents a probability distribution as a weighted sum of multiple Gaussian components. TheMLEmethod can be applied to estimate the parameters of these components (means, variances, and mixing coefficients) by maximizing the likelihood of the observed data. The Expectation- Maximization (EM) algorithm is typically used to perform MLE in GMMs because it can handle hidden (latent) variables representing the component assignments.
Exact Extract from HCIP-AI EI Developer V2.5:
"MLE, implemented through the EM algorithm, is commonly used to estimate the parameters of Gaussian mixture models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Gaussian Mixture Models

질문 # 48
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. 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.
B. 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.
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.

정답：A,B,C,D

설명：
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

질문 # 49
Vision transformer (ViT) performs well in image classification tasks. Which of the following is the main advantage of ViT?

A. The self-attention mechanism is used to capture global features of images, improving classification accuracy.
B. It achieves fast convergence without using pre-trained models.
C. It can handle small datasets with minimal labeling required.
D. It can process high-resolution images to enhance classification accuracy.

정답：A

설명：
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

질문 # 50
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. FALSE
B. TRUE

정답：A

설명：
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

질문 # 51
......

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