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H13-321_V2.5 Study Materials - H13-321_V2.5 Quiz Bootcamp & H13-321_V2.5 Qui

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H13-321_V2.5 Study Materials - H13-321_V2.5 Quiz Bootcamp & H13-321_V2.5 Qui

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q21-Q26):NEW QUESTION # 21
If a scanned document is not properly placed, and the text is tilted, it is difficult to recognize the characters in the document. Which of the following techniques can be used for correction in this case?
  • A. Perspective transformation
  • B. Rotational transformation
  • C. Grayscale transformation
  • D. Affine transformation
Answer: B,D
Explanation:
When text in scanned images is tilted,rotational transformationcan correct the angle of the text to align horizontally.Affine transformationcan correct tilt and skew by applying linear transformations such as rotation, scaling, and translation while preserving parallelism of lines. Perspective transformation (A) is used for correcting trapezoidal distortions, while grayscale transformation (B) only adjusts pixel intensity, not orientation.
Exact Extract from HCIP-AI EI Developer V2.5:
"Text skew correction can be achieved using rotation and affine transformations, aligning text baselines and improving OCR accuracy." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Transformation

NEW QUESTION # 22
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 # 23
Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?
  • A. Color image input is multi-channel, whereas text input is single-channel.
  • B. For CNN, there is no difference in handling text or image tasks.
  • C. CNNs are suitable for image tasks, but they perform poorly in text tasks.
  • D. When the CNN is used for text tasks, the kernel size must be the same as the number of word vector dimensions. This constraint, however, does not apply to image tasks.
Answer: A,D
Explanation:
In CNN usage:
* A:True - color images have multiple channels (e.g., RGB = 3), while text inputs are represented as sequences of word embeddings, typically single-channel in structure.
* B:True - in text tasks, the convolution kernel height must match the embedding dimension to capture complete token information, which is not a constraint in images.
* C:False - there are clear differences in handling between text and image data.
* D:False - CNNs can perform very well in text classification when used appropriately.
Exact Extract from HCIP-AI EI Developer V2.5:
"In text CNNs, convolution kernels span the entire embedding dimension, whereas in image CNNs, kernel size is independent of channel count." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: CNN in NLP

NEW QUESTION # 24
In natural language processing tasks, word vector evaluation is an important aspect for measuring the performance of a word embedding model. Which of the following statements about word vector evaluation are true?
  • A. Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
  • B. Extrinsic evaluation is the main method used for evaluating word vectors because it directly reflects the performance of word vectors in real-world application tasks.
  • C. The word analogy task evaluates the capability of word vectors in capturing semantic relationships between words, for example, by determining whether "king - man + woman = ?" is close to "queen".
  • D. Word similarity tasks typically employ manually labeled datasets to evaluate word vectors, compute the cosine similarity between word vectors, and compare it with the manual labeling result.
Answer: A,C,D
Explanation:
Word vector evaluation can be:
* Intrinsicirectly tests vector properties via word similarity and analogy tasks.
* Extrinsic:Tests in downstream applications.
* A:True - word similarity tasks use human-labeled datasets and cosine similarity.
* B:True - intrinsic evaluations include similarity and analogy tasks.
* C:True - analogy tests assess how well vectors capture semantic relationships.
* D:False - both intrinsic and extrinsic methods are valuable, but intrinsic methods are more common for initial evaluations.
Exact Extract from HCIP-AI EI Developer V2.5:
"Intrinsic evaluations (similarity, analogy) test embedding quality directly, while extrinsic evaluations measure impact on real tasks." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Word Vector Evaluation

NEW QUESTION # 25
The mAP evaluation metric in object detection combines accuracy and recall.
  • A. FALSE
  • B. TRUE
Answer: A
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 # 26
......
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