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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q39-Q44):

NEW QUESTION # 39
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).

A. FALSE
B. TRUE

Answer: B

Explanation:
A Gaussian mixture model represents a probability distribution as a weighted sum of multiple Gaussian components. TheMLEmethod can be applied to estimate the parameters of these components (means, variances, and mixing coefficients) by maximizing the likelihood of the observed data. The Expectation- Maximization (EM) algorithm is typically used to perform MLE in GMMs because it can handle hidden (latent) variables representing the component assignments.
Exact Extract from HCIP-AI EI Developer V2.5:
"MLE, implemented through the EM algorithm, is commonly used to estimate the parameters of Gaussian mixture models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Gaussian Mixture Models

NEW QUESTION # 40
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).

A. FALSE
B. TRUE

Answer: B

NEW QUESTION # 41
The mAP evaluation metric in object detection combines accuracy and recall.

A. FALSE
B. TRUE

Answer: A

Explanation:
ThemAP(mean Average Precision) metric in object detection combinesprecisionandrecall, not accuracy and recall. mAP is calculated by averaging the Average Precision (AP) across all classes in a dataset. Precision measures how many predicted positives are correct, while recall measures how many actual positives are identified. Accuracy, on the other hand, is a general metric for classification tasks and is less suitable for object detection where class imbalance and localization are important.
Exact Extract from HCIP-AI EI Developer V2.5:
"mAP evaluates object detection performance by considering both precision and recall across all classes, providing a balanced measure of detection accuracy and completeness." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection Metrics

NEW QUESTION # 42
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 # 43
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. 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).
C. Transformer models outperform RNN and CNN in processing long texts because they can effectively capture global dependencies.
D. Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.

Answer: A,B,C

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 # 44
......

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Noah Murphy Noah Murphy

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