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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q52-Q57):

NEW QUESTION # 52
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: C

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 # 53
Which of the following are required for the image object detection algorithm?

A. Confidence calculation
B. Object classification determination
C. Object contour calculation
D. Object location calculation

Answer: A,B,D

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 # 54
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. 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".
B. 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.
C. 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.
D. Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.

Answer: A,C,D

Explanation:
Word vector evaluation can be:
* Intrinsic:Directly 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 # 55
In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. In a Transformer model, there is customized LSTM with CNN layers.

A. FALSE
B. TRUE

Answer: A

Explanation:
TheTransformerarchitecture introduced in 2017 eliminates recurrence (RNN) and convolution entirely, relying solely on self-attention mechanisms and feed-forward layers. It does not contain LSTM or CNN components, which distinguishes it from previous sequence models.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Transformer architecture does not use RNNs or CNNs. It relies entirely on self-attention and feed- forward networks for sequence modeling." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Architecture Overview

NEW QUESTION # 56
Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?

A. When the CNN is used for text tasks, the kernel size must be the same as the number of word vector dimensions. This constraint, however, does not apply to image tasks.
B. Color image input is multi-channel, whereas text input is single-channel.
C. For CNN, there is no difference in handling text or image tasks.
D. CNNs are suitable for image tasks, but they perform poorly in text tasks.

Answer: A,B

Explanation:
In CNN usage:
* A:True - color images have multiple channels (e.g., RGB = 3), while text inputs are represented as sequences of word embeddings, typically single-channel in structure.
* B:True - in text tasks, the convolution kernel height must match the embedding dimension to capture complete token information, which is not a constraint in images.
* C:False - there are clear differences in handling between text and image data.
* D:False - CNNs can perform very well in text classification when used appropriately.
Exact Extract from HCIP-AI EI Developer V2.5:
"In text CNNs, convolution kernels span the entire embedding dimension, whereas in image CNNs, kernel size is independent of channel count." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: CNN in NLP

NEW QUESTION # 57
......

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100% Pass Huawei - Reliable H13-321_V2.5 - Vce HCIP-AI-EI Developer V2.5 File

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q52-Q57):

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