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H13-321_V2.5考古题推薦 & H13-321_V2.5考題套裝

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H13-321_V2.5考古题推薦 & H13-321_V2.5考題套裝

Posted at 3 hour before      View：4 | Replies：0        Print      Only Author   [Copy Link] 1#
此外，這些NewDumps H13-321_V2.5考試題庫的部分內容現在是免費的：https://drive.google.com/open?id=105RDWq_NPjKWcfjXFMNn8jPb0IQRHzq6
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最新的 HCIP-AI EI Developer H13-321_V2.5 免費考試真題 (Q14-Q19):問題 #14
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. Rotational transformation
  • B. Affine transformation
  • C. Grayscale transformation
  • D. Perspective transformation
答案：A,B
解題說明：
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

問題 #15
What are the advantages of deep learning-based speech recognition algorithms?
  • A. Forced alignment of annotated data
  • B. Automated feature extraction
  • C. No data training
  • D. End-to-end task processing
答案：B,D
解題說明：
Deep learning-based speech recognition offers two key advantages over traditional approaches:
* Automated feature extraction (B):Neural networks can directly learn features from raw or lightly processed audio without manual engineering of MFCCs or filter banks.
* End-to-end task processing (C):Models like CTC-based networks or attention-based architectures can map audio inputs directly to text outputs without intermediate models like GMM-HMM.
Options A and D are incorrect because forced alignment is part of traditional GMM-HMM systems, and deep learning still requires training with large datasets.
Exact Extract from HCIP-AI EI Developer V2.5:
"Deep learning models support automatic feature extraction and can implement end-to-end mapping from speech signals to text outputs." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: End-to-End Speech Recognition

問題 #16
Maximum likelihood estimation (MLE) requires knowledge of the sample data's distribution type.
  • A. FALSE
  • B. TRUE
答案：B
解題說明：
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

問題 #17
The basic operations of morphological processing include dilation and erosion. These operations can be combined to achieve practical algorithms such as opening and closing operations.
  • A. FALSE
  • B. TRUE
答案：B
解題說明：
Morphological processing in image analysis is used to process binary or grayscale images based on shape.
* Dilation:Expands object boundaries, useful for filling small holes.
* Erosion:Shrinks object boundaries, useful for removing noise.By combining them:
* Opening:Erosion followed by dilation (removes small objects/noise).
* Closingilation followed by erosion (fills small holes).
Exact Extract from HCIP-AI EI Developer V2.5:
"Morphological processing is based on dilation and erosion. Opening and closing are composite operations derived from these two to handle noise removal and hole filling." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Morphological Image Processing

問題 #18
Which of the following is not an algorithm for training word vectors?
  • A. TextCNN
  • B. BERT
  • C. FastText
  • D. Word2Vec
答案：A
解題說明：
* Word2VecandFastTextare neural network-based algorithms designed for generating dense vector representations of words.
* BERTis a transformer-based language model that also generates contextualized word embeddings.
* TextCNN, however, is a text classification model, not a word vector training algorithm. It uses convolutional neural networks to extract features from already vectorized text but does not learn static word embeddings in the same sense as Word2Vec or FastText.
Exact Extract from HCIP-AI EI Developer V2.5:
"Word2Vec, FastText, and BERT can be used to train word embeddings. TextCNN is a classification model that uses embeddings but does not train them as its primary function." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Word Vector Representation

問題 #19
......
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