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[Hardware] Test H13-321_V2.5 Answers | H13-321_V2.5 Test Duration

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【Hardware】 Test H13-321_V2.5 Answers | H13-321_V2.5 Test Duration

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q59-Q64):NEW QUESTION # 59
Which of the following is a learning algorithm used for Markov chains?
  • A. Exhaustive search
  • B. Viterbi algorithm
  • C. Forward-backward algorithm
  • D. Baum-Welch algorithm
Answer: D
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 # 60
The attention mechanism in foundation model architectures allows the model to focus on specific parts of the input data. Which of the following steps are key components of a standard attention mechanism?
  • A. Calculate the dot product similarity between the query and key vectors to obtain attention scores.
  • B. Apply a non-linear mapping to the result obtained after the weighted summation.
  • C. Compute the weighted sum of the value vectors using the attention weights.
  • D. Normalize the attention scores to obtain attention weights.
Answer: A,C,D
Explanation:
The standardattention mechanisminvolves:
* Computing attention scores via the dot product of query and key vectors (A).
* Applying a normalization function (typically softmax) to obtain attention weights (D).
* Using these weights to compute a weighted sum of the value vectors (B).OptionCis not a standard step
- non-linear mappings are not applied after the weighted sum in the basic attention formula.
Exact Extract from HCIP-AI EI Developer V2.5:
"Attention computes dot products between query and key, normalizes scores with softmax, and uses them to weight value vectors." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Attention Mechanism Fundamentals

NEW QUESTION # 61
The development of large models should comply with ethical principles to ensure the legal, fair, and transparent use of data.
  • A. FALSE
  • B. TRUE
Answer: B
Explanation:
Ethical AI development requires ensuring that large models are trained and deployed in a way that respects laws, fairness, and transparency. This includes preventing bias, ensuring user privacy, protecting intellectual property, and being transparent about data usage and decision-making processes.
Exact Extract from HCIP-AI EI Developer V2.5:
"The development and deployment of large models must follow ethical principles to ensure legal, fair, and transparent use of data, avoiding bias and misuse." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Ethical AI Practices

NEW QUESTION # 62
In the field of deep learning, which of the following activation functions has a derivative not greater than 0.5?
  • A. Tanh
  • B. Sigmoid
  • C. ReLU
  • D. SeLU
Answer: B
Explanation:
Thesigmoidactivation function maps inputs to the range (0, 1) and has a maximum derivative of 0.25 at x=0.
This derivative value is always # 0.5, making it the correct choice here. While sigmoid is historically used in neural networks, it suffers from the vanishing gradient problem for large positive or negative inputs due to its small derivative values. Other functions such as ReLU, Tanh, and SeLU have different derivative behaviors, with ReLU having a derivative of 1 for positive inputs, Tanh having derivatives up to 1, and SeLU designed for self-normalizing networks with derivatives potentially greater than 0.5.
Exact Extract from HCIP-AI EI Developer V2.5:
"Sigmoid compresses values into the (0,1) range, with its maximum derivative being 0.25, which is always less than 0.5." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Activation Functions in Neural Networks

NEW QUESTION # 63
What are the advantages of deep learning-based speech recognition algorithms?
  • A. Automated feature extraction
  • B. No data training
  • C. Forced alignment of annotated data
  • D. End-to-end task processing
Answer: A,D
Explanation:
Deep learning-based speech recognition offers two key advantages over traditional approaches:
* Automated feature extraction (B):Neural networks can directly learn features from raw or lightly processed audio without manual engineering of MFCCs or filter banks.
* End-to-end task processing (C):Models like CTC-based networks or attention-based architectures can map audio inputs directly to text outputs without intermediate models like GMM-HMM.
Options A and D are incorrect because forced alignment is part of traditional GMM-HMM systems, and deep learning still requires training with large datasets.
Exact Extract from HCIP-AI EI Developer V2.5:
"Deep learning models support automatic feature extraction and can implement end-to-end mapping from speech signals to text outputs." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: End-to-End Speech Recognition

NEW QUESTION # 64
......
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