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Valid H13-321_V2.5 Mock Exam | H13-321_V2.5 Exam Passing Score

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

NEW QUESTION # 39
The technologies underlying ModelArts support a wide range of heterogeneous compute resources, allowing you to flexibly use the resources that fit your needs.

A. TRUE
B. FALSE

Answer: A

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 # 40
Which of the following has never been used as a method in the history of NLP?

A. Recursion-based method
B. Deep learning-based method
C. Statistics-based method
D. Rule-based method

Answer: A

Explanation:
Historically, NLP has evolved through three main methodological phases:
* Rule-based methods- used in early systems, relying on manually crafted grammar and lexicons.
* Statistics-based methods- introduced probabilistic models such as HMMs and n-grams.
* Deep learning-based methods- using neural networks, transformers, and embeddings.
A "recursion-based method" has never been recognized as a distinct NLP methodology, even though recursion can appear in linguistic theory, it is not a primary computational approach in NLP history.
Exact Extract from HCIP-AI EI Developer V2.5:
"The evolution of NLP includes rule-based, statistical, and deep learning-based methods. Recursion-based approaches are not considered a formal method in NLP development history." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: NLP Development History

NEW QUESTION # 41
Transformer models outperform LSTM when analyzing and processing long-distance dependencies, making them more effective for sequence data processing.

A. TRUE
B. FALSE

Answer: A

Explanation:
Transformers, usingself-attention, can capture dependencies between any two positions in a sequence directly, regardless of distance. LSTMs, despite gating mechanisms, process sequences step-by-step and may struggle with very long dependencies due to vanishing gradients. This makes Transformers more efficient and accurate for tasks involving long-range context, such as document summarization or translation.
Exact Extract from HCIP-AI EI Developer V2.5:
"Transformers excel in modeling long-distance dependencies because self-attention relates all positions in a sequence simultaneously, unlike recurrent models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer vs. RNN Performance

NEW QUESTION # 42
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. 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).
B. Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.
C. 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.
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 # 43
The jieba ------() method can be used for word segmentation.

Answer:

Explanation:
cut
Explanation:
In Python'sjiebalibrary, the cut() method is used for Chinese word segmentation. It splits a given sentence into individual words based on probabilistic models and a dictionary. The method supports both precise mode and full mode, with precise mode being the default for balanced accuracy and completeness.
Exact Extract from HCIP-AI EI Developer V2.5:
"The jieba.cut() method segments Chinese text into words, supporting multiple modes for different application needs." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Chinese Word Segmentation Tools

NEW QUESTION # 44
......

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Will Gray Will Gray

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