Title: Latest Upload H13-321_V2.5 Valid Dumps Ppt - Huawei Dumps HCIP-AI-EI Developer V [Print This Page] Author: danston530 Time: 5 hour before Title: Latest Upload H13-321_V2.5 Valid Dumps Ppt - Huawei Dumps HCIP-AI-EI Developer V P.S. Free & New H13-321_V2.5 dumps are available on Google Drive shared by VCE4Dumps: https://drive.google.com/open?id=1RzfT7lM-P4pyRL6nvFAOuL4dlVZ9j9CE
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Dumps H13-321_V2.5 Download & H13-321_V2.5 New Test BootcampThe user-friendly interface of H13-321_V2.5 Dumps (desktop & web-based) will make your preparation effective. The VCE4Dumps ensures that the H13-321_V2.5 practice exam will make you competent enough to crack the in-demand H13-321_V2.5 examination on the first attempt. Real Huawei H13-321_V2.5 dumps of VCE4Dumps come in PDF format as well. Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q49-Q54):NEW QUESTION # 49
What are the adjacency relationships between two pixels whose coordinates are (21,13) and (22,12)?
A. 4-adjacency
B. No adjacency relationship
C. 8-adjacency
D. Diagonal adjacency
Answer: C,D
Explanation:
Pixel adjacency describes how pixels are connected:
* 4-adjacencyixels share a side (up, down, left, right).
* Diagonal adjacencyixels touch at a corner.
* 8-adjacency:Combination of 4-adjacency and diagonal adjacency.
Given coordinates (21,13) and (22,12), the pixels differ by 1 in both x and y directions, meaning they meet at a corner - this isdiagonal adjacency. Since 8-adjacency includes both side and diagonal adjacency, they are also8-adjacent.
Exact Extract from HCIP-AI EI Developer V2.5:
"In 8-adjacency, pixels are considered neighbors if they are connected horizontally, vertically, or diagonally.
Diagonal adjacency occurs when pixels touch at a corner."
Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Digital Image Basics
NEW QUESTION # 50
In cases where the bright and dark areas of an image are too extreme, which of the following techniques can be used to improve the image?
A. Grayscale compression
B. Gamma correction
C. Inversion
D. Grayscale stretching
Answer: B
Explanation:
When the contrast between bright and dark areas is extreme,gamma correctionis effective in adjusting luminance in a non-linear way to balance these extremes.
* If# < 1, dark areas are brightened, highlights are compressed.
* If# > 1, bright areas are emphasized, shadows are compressed.Other methods like grayscale stretching and compression target linear contrast changes, while inversion flips pixel values but doesn't balance extreme light/dark ranges effectively.
Exact Extract from HCIP-AI EI Developer V2.5:
"Gamma correction adjusts image brightness non-linearly, suitable for correcting overly bright or overly dark regions, improving overall visibility." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Enhancement
NEW QUESTION # 51
Which of the following statements about the functions of layer normalization and residual connection in the Transformer is true?
A. In shallow networks, residual connections are beneficial, but they aggravate the vanishing gradient problem in deep networks.
B. Residual connections and layer normalization help prevent vanishing gradients and exploding gradients in deep networks.
C. Layer normalization accelerates model convergence and does not affect model stability.
D. Residual connections primarily add depth to the model but do not aid in gradient propagation.
Answer: B
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 # 52
The image saturation can be enhanced by processing the ________ component of the HSV color space. (Enter H, S, or V.) Answer:
Explanation:
S
Explanation:
In the HSV (Hue, Saturation, Value) color model:
* Hrepresents hue (color type).
* Srepresents saturation (color intensity or vividness).
* Vrepresents brightness.
To enhance saturation in an image, adjustments are made to theS component. Increasing S increases the color vividness, making the image appear more vibrant, while reducing S moves colors toward grayscale. This approach is widely used in image enhancement tasks, especially in object recognition and segmentation, where vivid colors improve feature contrast.
Exact Extract from HCIP-AI EI Developer V2.5:
"In HSV color space, saturation (S) describes the vividness of colors. Increasing the S value enhances saturation, making colors more intense, while decreasing it makes them closer to gray." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Processing Basics
NEW QUESTION # 53
Transformer models outperform LSTM when analyzing and processing long-distance dependencies, making them more effective for sequence data processing.
A. TRUE
B. FALSE
Answer: A
Explanation:
Transformers, usingself-attention, can capture dependencies between any two positions in a sequence directly, regardless of distance. LSTMs, despite gating mechanisms, process sequences step-by-step and may struggle with very long dependencies due to vanishing gradients. This makes Transformers more efficient and accurate for tasks involving long-range context, such as document summarization or translation.
Exact Extract from HCIP-AI EI Developer V2.5:
"Transformers excel in modeling long-distance dependencies because self-attention relates all positions in a sequence simultaneously, unlike recurrent models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer vs. RNN Performance
NEW QUESTION # 54
......
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ixels share a side (up, down, left, right).