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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q47-Q52):
NEW QUESTION # 47
In NLP tasks, transformer models perform well in multiple tasks due to their self-attention mechanism and parallel computing capability. Which of the following statements about transformer models are true?
- A. Multi-head attention is the core component of a transformer model. It computes multiple attention heads in parallel to capture semantic information in different subspaces.
- B. Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.
- C. A transformer model directly captures the dependency between different positions in the input sequence through the self-attention mechanism, without using the recurrent neural network (RNN) or convolutional neural network (CNN).
- D. Transformer models outperform RNN and CNN in processing long texts because they can effectively capture global dependencies.
Answer: A,C,D
Explanation:
Transformers are designed for sequence modeling without recurrence or convolution.
* A:True - self-attention captures global dependencies efficiently, outperforming RNNs/CNNs in long text processing.
* B:True - multi-head attention computes multiple attention projections in parallel.
* C:True - the architecture is purely attention-based.
* D:False - positional encoding isrequiredbecause self-attention does not inherently encode sequence order.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Transformer uses self-attention to model dependencies and multi-head attention to capture features in different subspaces. Positional encoding must be added to preserve sequence order." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Architecture
NEW QUESTION # 48
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
Answer: B
Explanation:
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).
* Closing:Dilation 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
NEW QUESTION # 49
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. Affine transformation
- B. Rotational transformation
- C. Perspective transformation
- D. Grayscale transformation
Answer: A,B
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 # 50
Which of the following is not an acoustic feature of speech?
- A. Duration
- B. Semantics
- C. Frequency
- D. Amplitude
Answer: B
Explanation:
In speech signal processing,acoustic featuresdescribe measurable physical properties of sound waves, such as duration(time length),frequency(pitch), andamplitude(loudness). These features are used in speech recognition and speaker identification systems.
Semantics, on the other hand, refers to the meaning of speech - a linguistic attribute, not an acoustic property. Therefore, it is not classified as an acoustic feature.
Exact Extract from HCIP-AI EI Developer V2.5:
"Speech features include duration, frequency, and amplitude. These are acoustic characteristics, distinct from semantic information, which relates to language meaning." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Speech Feature Extraction
NEW QUESTION # 51
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. Speech recognition
- C. Image classification
- 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 # 52
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