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[General] High-quality Test H13-321_V2.5 Cram Review - Pass H13-321_V2.5 Exam

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【General】 High-quality Test H13-321_V2.5 Cram Review - Pass H13-321_V2.5 Exam

Posted at 2/13/2026 04:12:34      View：60 | Replies：2        Print      Only Author   [Copy Link] 1#
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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q19-Q24):NEW QUESTION # 19
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 # 20
------- is a model that uses a convolutional neural network (CNN) to classify texts.
Answer:
Explanation:
Text CNN
Explanation:
Text CNN applies convolutional layers directly to text data represented as word embeddings. By using multiple kernel sizes, Text CNN captures features from n-grams of varying lengths. These features are pooled and passed to fully connected layers for classification tasks such as sentiment analysis or spam detection.
Exact Extract from HCIP-AI EI Developer V2.5:
"Text CNN applies convolution and pooling over word embeddings to extract local features for text classification." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: CNN Applications in NLP

NEW QUESTION # 21
The mAP evaluation metric in object detection combines accuracy and recall.
  • A. TRUE
  • B. FALSE
Answer: B
Explanation:
ThemAP(mean Average Precision) metric in object detection combinesprecisionandrecall, not accuracy and recall. mAP is calculated by averaging the Average Precision (AP) across all classes in a dataset. Precision measures how many predicted positives are correct, while recall measures how many actual positives are identified. Accuracy, on the other hand, is a general metric for classification tasks and is less suitable for object detection where class imbalance and localization are important.
Exact Extract from HCIP-AI EI Developer V2.5:
"mAP evaluates object detection performance by considering both precision and recall across all classes, providing a balanced measure of detection accuracy and completeness." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection Metrics

NEW QUESTION # 22
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 # 23
In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. In a Transformer model, there is customized LSTM with CNN layers.
  • A. TRUE
  • B. FALSE
Answer: B
Explanation:
TheTransformerarchitecture introduced in 2017 eliminates recurrence (RNN) and convolution entirely, relying solely on self-attention mechanisms and feed-forward layers. It does not contain LSTM or CNN components, which distinguishes it from previous sequence models.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Transformer architecture does not use RNNs or CNNs. It relies entirely on self-attention and feed- forward networks for sequence modeling." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Architecture Overview

NEW QUESTION # 24
......
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Posted at 2/14/2026 20:18:56        Only Author  2#
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