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[General] H13-321_V2.5 Free Download, H13-321_V2.5 Interactive EBook

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【General】 H13-321_V2.5 Free Download, H13-321_V2.5 Interactive EBook

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q23-Q28):NEW QUESTION # 23
Which of the following are required for the image object detection algorithm?
  • A. Object location calculation
  • B. Object classification determination
  • C. Confidence calculation
  • D. Object contour calculation
Answer: A,B,C
Explanation:
An object detection system must:
* Classifythe detected object (A).
* Locatethe object by generating bounding box coordinates (C).
* Estimate confidencescores indicating prediction reliability (D).
Object contour calculation (B) is a separate task often related toinstance segmentation, not general object detection.
Exact Extract from HCIP-AI EI Developer V2.5:
"Object detection includes classification, bounding box localization, and confidence score prediction.
Contour detection belongs to segmentation tasks."
Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection Workflow

NEW QUESTION # 24
Which of the following statements about the levels of natural language understanding are true?
  • A. Semantic analysis is to analyze the structure of sentences and phrases to find out the relationship between words and phrases, as well as their functions in sentences.
  • B. Pragmatic analysis is to study the influence of the language's external environment on the language users.
  • C. Lexical analysis is to find the lexemes of a word and obtain linguistic information from them.
  • D. Speech analysis involves distinguishing independent phonemes from a speech stream based on phoneme rules, and then identifying syllables and their lexemes or words according to the phoneme form rules.
  • E. Syntactic analysis is to find out the meaning of words, structural meaning, their combined meaning, so as to determine the true meaning or concept expressed by a language.
Answer: B,C,D
Explanation:
* A:Incorrect - description given matches semantic analysis, not syntactic analysis.
* B:Incorrect - description given matches syntactic analysis, not semantic analysis.
* C:Correct - speech analysis focuses on phoneme recognition and word identification.
* D:Correct - lexical analysis identifies lexemes and retrieves their linguistic details.
* E:Correct - pragmatic analysis studies language use in context and environment.
Exact Extract from HCIP-AI EI Developer V2.5:
"Natural language understanding involves lexical, syntactic, semantic, speech, and pragmatic analyses, each focusing on different layers of language processing." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Levels of Language Understanding

NEW QUESTION # 25
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. Object detection
  • B. Speech recognition
  • C. Video analytics
  • D. Image classification
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 # 26
What type of task is viewed when using the Seq2Seq model in speech recognition?
  • A. Clustering task
  • B. Dimensionality reduction task
  • C. Regression task
  • D. Classification task
Answer: D
Explanation:
The Seq2Seq (sequence-to-sequence) model converts an input sequence into an output sequence. In speech recognition, the input is a sequence of acoustic features, and the output is a sequence of text tokens. This is essentially aclassification taskbecause each output token is classified into a predefined vocabulary set.
Although the output is sequential, each position in the output sequence involves a classification decision.
Exact Extract from HCIP-AI EI Developer V2.5:
"In speech recognition, Seq2Seq models classify each output token from a fixed vocabulary, making the overall problem a sequence of classification tasks." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Sequence Models in Speech Recognition

NEW QUESTION # 27
Which of the following statements about the functions of layer normalization and residual connection in the Transformer is true?
  • A. Residual connections and layer normalization help prevent vanishing gradients and exploding gradients in deep networks.
  • B. In shallow networks, residual connections are beneficial, but they aggravate the vanishing gradient problem 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: A
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 # 28
......
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