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Pass Your Huawei H13-321_V2.5: HCIP-AI-EI Developer V2.5 Exam with Correct Latest H13-321_V2.5 Dumps Sheet SurelyI am glad to introduce a secret weapon for all of the candidates to pass the exam as well as get the related certification without any more ado-- our H13-321_V2.5 study materials. You can only get the most useful and efficient study materials with the most affordable price. With our H13-321_V2.5 practice test, you only need to spend 20 to 30 hours in preparation since there are all essence contents in our H13-321_V2.5 Study Materials. What's more, if you need any after service help on our H13-321_V2.5 exam guide, our after service staffs will always offer the most thoughtful service for you. Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q22-Q27):NEW QUESTION # 22
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. Each header's query, key, and value undergo a shared linear transformation to obtain them.
C. The multi-head attention mechanism captures information about different subspaces within a sequence.
D. The concatenated output is fed directly into the multi-headed attention mechanism.
Answer: A,C
NEW QUESTION # 23
The U-Net uses an upsampling mechanism and has a fully-connected layer.
A. FALSE
B. TRUE
Answer: A
Explanation:
U-Net is a convolutional neural network architecture designed for biomedical image segmentation. It consists of a contracting path for feature extraction and an expansive path for precise localization, usingupsamplingin the decoding path. However, U-Netdoes not include fully-connected layers; instead, it uses only convolutional layers to maintain spatial information. Removing fully-connected layers ensures the network can handle images of varying sizes without requiring fixed input dimensions.
Exact Extract from HCIP-AI EI Developer V2.5:
"U-Net architecture is fully convolutional and avoids fully-connected layers to preserve spatial resolution, relying on upsampling in the decoder path for segmentation tasks." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Semantic Segmentation Networks
NEW QUESTION # 24
Which of the following is a learning algorithm used for Markov chains?
A. Viterbi algorithm
B. Baum-Welch algorithm
C. Forward-backward algorithm
D. Exhaustive search
Answer: B
Explanation:
TheBaum-Welch algorithmis a special case of the Expectation-Maximization (EM) algorithm used to train Hidden Markov Models (HMMs). It estimates model parameters (transition probabilities, emission probabilities) when the training data is incomplete or hidden.
* Viterbi algorithmis for decoding, not training.
* Forward-backward algorithmis part of Baum-Welch's expectation step but is not a standalone training method.
* Exhaustive searchis not a standard HMM training algorithm.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Baum-Welch algorithm iteratively optimizes HMM parameters using forward and backward probability computations until convergence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: HMM Training Algorithms
NEW QUESTION # 25
Which of the following are the impacts of the development of large models?
A. Model pre-training costs will be reduced
B. The accuracy and efficiency of natural language processing tasks will improve
C. Large models will completely replace small and domain-specific models
D. Data privacy and security issues will be exacerbated
Answer: B,D
Explanation:
The emergence of large AI models (e.g., GPT, Pangu, BERT) has led to:
* C:Improved accuracy and efficiency in NLP and other AI tasks because of their ability to capture deep semantic and contextual information.
* D:Increased data privacy and security concerns, as large models require massive datasets which may contain sensitive or proprietary information.Ais false - large models increase pre-training costs.Bis false - small and domain-specific models still play important roles due to efficiency and deployment constraints.
Exact Extract from HCIP-AI EI Developer V2.5:
"Large models improve task performance but raise privacy and security concerns. They do not necessarily reduce training cost or eliminate the need for smaller models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Large Model Trends and Challenges
NEW QUESTION # 26
A text classification task has only one final output, while a sequence labeling task has an output in each input position.
A. FALSE
B. TRUE
Answer: B
Explanation:
In NLP:
* Text classification(e.g., sentiment analysis) predicts a single label for the entire input sequence.
* Sequence labeling(e.g., Named Entity Recognition, Part-of-Speech tagging) produces an output label for each token or position in the input sequence.This distinction is important for selecting appropriate model architectures and loss functions.
Exact Extract from HCIP-AI EI Developer V2.5:
"Text classification assigns one label to the whole text, whereas sequence labeling assigns a label to each token in the sequence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: NLP Task Categories
NEW QUESTION # 27
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