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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q18-Q23):

NEW QUESTION # 18
Which of the following statements are true about the differences between using convolutional neural networks (CNNs) in text tasks and image tasks?

A. Color image input is multi-channel, whereas text input is single-channel.
B. 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.
C. CNNs are suitable for image tasks, but they perform poorly in text tasks.
D. For CNN, there is no difference in handling text or image 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 # 19
The technologies underlying ModelArts support a wide range of heterogeneous compute resources, allowing you to flexibly use the resources that fit your needs.

A. FALSE
B. TRUE

Answer: B

Explanation:
ModelArts is built to support a variety of compute resources, including CPUs, GPUs, and Ascend AI processors. This heterogeneous resource pool allows users to select the hardware that best matches their training or inference requirements, ensuring cost efficiency and optimal performance for different workloads.
Exact Extract from HCIP-AI EI Developer V2.5:
"ModelArts supports heterogeneous compute environments, enabling selection among CPUs, GPUs, and Ascend processors for flexible AI development." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: ModelArts Infrastructure

NEW QUESTION # 20
What type of task is viewed when using the Seq2Seq model in speech recognition?

A. Classification task
B. Regression task
C. Clustering task
D. Dimensionality reduction task

Answer: A

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 # 21
Which of the following statements about the multi-head attention mechanism of the Transformer are true?

A. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
B. The multi-head attention mechanism captures information about different subspaces within a sequence.
C. Each header's query, key, and value undergo a shared linear transformation to obtain them.
D. The concatenated output is fed directly into the multi-headed attention mechanism.

Answer: A,B

Explanation:
In themulti-head attentionmechanism:
* A:True - the input embedding dimension is split across multiple heads, so each head operates on a lower-dimensional subspace before concatenation.
* B:True - having multiple attention heads allows the model to attend to information from different representation subspaces simultaneously.
* C:False - each head has its own learned linear transformations for queries, keys, and values.
* D:False - after concatenation, the result is passed through a final linear projection, not fed back into the attention module directly.
Exact Extract from HCIP-AI EI Developer V2.5:
"Multi-head attention divides the embedding dimension across heads to learn from multiple subspaces in parallel, then concatenates and linearly projects the result." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Multi-Head Attention

NEW QUESTION # 22
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. TRUE
B. FALSE

Answer: B

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 # 23
......

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Huawei certification H13-321_V2.5 exam targeted training

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