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[General] H13-321_V2.5 Technical Training & New Exam H13-321_V2.5 Braindumps

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【General】 H13-321_V2.5 Technical Training & New Exam H13-321_V2.5 Braindumps

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q11-Q16):NEW QUESTION # 11
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. Grayscale transformation
  • B. Affine transformation
  • C. Perspective transformation
  • D. Rotational 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 # 12
Huawei Cloud ModelArts is a one-stop AI development platform that supports multiple AI scenarios. Which of the following scenarios are supported by ModelArts?
  • A. Video analytics
  • B. Image classification
  • C. Object detection
  • D. Speech recognition
Answer: A,B,C,D
Explanation:
ModelArts provides an integrated environment for data labeling, model training, deployment, and management, supporting various AI application scenarios:
* Image classificationfor categorizing visual content.
* Object detectionfor locating and identifying multiple objects in images or video frames.
* Speech recognitionfor converting speech to text.
* Video analyticsfor automated video content analysis.
Exact Extract from HCIP-AI EI Developer V2.5:
"ModelArts supports a wide range of AI tasks including image classification, object detection, speech recognition, and intelligent video analytics." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: ModelArts Overview

NEW QUESTION # 13
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. 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.
  • B. 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.
  • 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 vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
Answer: B,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 # 14
------- 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 # 15
Which of the following is a learning algorithm used for Markov chains?
  • A. Exhaustive search
  • B. Forward-backward algorithm
  • C. Baum-Welch algorithm
  • D. Viterbi algorithm
Answer: C
Explanation:
TheBaum-Welch algorithmis a special case of the Expectation-Maximization (EM) algorithm used to train Hidden Markov Models (HMMs). It estimates model parameters (transition probabilities, emission probabilities) when the training data is incomplete or hidden.
* Viterbi algorithmis for decoding, not training.
* Forward-backward algorithmis part of Baum-Welch's expectation step but is not a standalone training method.
* Exhaustive searchis not a standard HMM training algorithm.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Baum-Welch algorithm iteratively optimizes HMM parameters using forward and backward probability computations until convergence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: HMM Training Algorithms

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