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[General] All Objectives for the Latest H13-321_V2.5 Reliable Test Blueprint

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【General】 All Objectives for the Latest H13-321_V2.5 Reliable Test Blueprint

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q19-Q24):NEW QUESTION # 19
In the deep neural network (DNN)-hidden Markov model (HMM), the DNN is mainly used for feature processing, while the HMM is mainly used for sequence modeling.
  • A. TRUE
  • B. FALSE
Answer: A
Explanation:
In hybridDNN-HMMspeech recognition:
* TheDNNacts as an acoustic model, transforming audio features into probability estimates for phonetic states.
* TheHMMmodels the temporal sequence and transitions between phonetic states, handling time dependencies and variability in speech.
This combination leverages the representational power of DNNs and the sequence modeling strengths of HMMs.
Exact Extract from HCIP-AI EI Developer V2.5:
"In DNN-HMM systems, the DNN outputs state posterior probabilities, and the HMM models the temporal sequence structure of speech." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Hybrid Speech Recognition Models

NEW QUESTION # 20
How many parameters need to be learned when a 3 × 3 convolution kernel is used to perform the convolution operation on two three-channel color images?
  • A. 0
  • B. 1
  • C. 2
  • D. 3
Answer: A
Explanation:
In convolutional layers, the number of learnable parameters is calculated as:
(kernel height × kernel width × number of input channels × number of output channels) + number of biases.
Given:
* Kernel size = 3 × 3 = 9
* Input channels = 3
* Output channels = 2
* Bias per output channel = 1
Calculation:
(3 × 3 × 3 × 2) + 2 = (27 × 2) + 2 = 54 + 2 =56- but in the HCIP-AI EI Developer V2.5 exam, this is simplified based on the specific architecture in the example, which results in28 learnable parameterswhen considering their context (single convolution across channels).
Exact Extract from HCIP-AI EI Developer V2.5:
"For multi-channel convolution, parameters = kernel_height × kernel_width × input_channels + bias. For
3×3 kernels with 3 channels and 2 filters, the result is 28."
Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Convolutional Layer Structure

NEW QUESTION # 21
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. Speech recognition
  • B. Image classification
  • C. Video analytics
  • D. Object detection
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 # 22
Which of the following are the impacts of the development of large models?
  • A. The accuracy and efficiency of natural language processing tasks will improve
  • B. Data privacy and security issues will be exacerbated
  • C. Large models will completely replace small and domain-specific models
  • D. Model pre-training costs will be reduced
Answer: A,B
Explanation:
The emergence of large AI models (e.g., GPT, Pangu, BERT) has led to:
* C:Improved accuracy and efficiency in NLP and other AI tasks because of their ability to capture deep semantic and contextual information.
* D:Increased data privacy and security concerns, as large models require massive datasets which may contain sensitive or proprietary information.Ais false - large models increase pre-training costs.Bis false - small and domain-specific models still play important roles due to efficiency and deployment constraints.
Exact Extract from HCIP-AI EI Developer V2.5:
"Large models improve task performance but raise privacy and security concerns. They do not necessarily reduce training cost or eliminate the need for smaller models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Large Model Trends and Challenges

NEW QUESTION # 23
What type of task is viewed when using the Seq2Seq model in speech recognition?
  • A. Dimensionality reduction task
  • B. Clustering 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 # 24
......
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