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Free PDF Quiz Huawei - H13-321_V2.5 - Valid Latest HCIP-AI-EI Developer V2.5 Test Guide

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q17-Q22):

NEW QUESTION # 17
Which of the following ModelArts training parameters is used to customize hyperparameters?

A. Compute Nodes
B. Resource Pool
C. Algorithm Type
D. Hyperparameter

Answer: D

Explanation:
In Huawei Cloud ModelArts training jobs, theHyperparameterparameter is explicitly designed to allow users to define custom training settings, such as learning rate, batch size, and number of epochs.
* Algorithm Typespecifies the model algorithm.
* Resource Poolselects the computational environment.
* Compute Nodesdetermines the number of nodes used for training.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Hyperparameter field in ModelArts allows users to define and pass custom training parameters to the algorithm for tuning performance." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: ModelArts Training Job Parameters

NEW QUESTION # 18
In the image recognition algorithm, the structure design of the convolutional layer has a great impact on its performance. Which of the following statements are true about the structure and mechanism of the convolutional layer? (Transposed convolution is not considered.)

A. The convolutional layer uses parameter sharing so that features at different positions share the same group of parameters. This reduces the number of network parameters required but reduces the expression capabilities of models.
B. In the convolutional layer, each neuron only collects some information. This effectively reduces the memory required.
C. A stride in the convolutional layer can control the spatial resolution of the output feature map. A larger stride indicates a smaller output feature map and simpler calculation.
D. The convolutional layer slides over the input feature map using a convolution kernel of a fixed size to extract local features without explicitly defining their features.

Answer: A,B,C,D

Explanation:
The convolutional layer in CNNs is optimized for spatial feature extraction:
* Local connectivity(A) reduces computation and memory usage.
* Parameter sharing(B) reduces the number of learnable parameters and helps prevent overfitting.
* Stride control(C) allows adjusting the output resolution and computational cost.
* Sliding kernel operation(D) extracts local patterns without manual feature definition.
Exact Extract from HCIP-AI EI Developer V2.5:
"CNN convolutional layers leverage local connectivity, parameter sharing, and stride control to efficiently extract local features, reducing computational requirements compared to fully-connected layers." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Convolutional Neural Networks

NEW QUESTION # 19
Which of the following statements about the standard normal distribution are true?

A. The variance is 0.
B. The mean is 1.
C. The mean is 0.
D. The variance is 1.

Answer: C,D

Explanation:
Astandard normal distributionis a special case of the normal distribution with:
* Mean (#) = 0
* Variance (#²) = 1This standardization is widely used in statistics and machine learning to normalize features for improved model convergence. Statements A and B are incorrect because variance is never 0 in a valid distribution, and the mean is 0, not 1.
Exact Extract from HCIP-AI EI Developer V2.5:
"The standard normal distribution is defined with # = 0 and #² = 1, providing a normalized scale for statistical analysis." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Probability and Statistics Fundamentals

NEW QUESTION # 20
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. Transformer models outperform RNN and CNN in processing long texts because they can effectively capture global dependencies.
B. 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.
C. Positional encoding is optional in a transformer model because the self-attention mechanism can naturally process the order information of sequences.
D. 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).

Answer: A,B,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 # 21
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).

A. TRUE
B. FALSE

Answer: A

Explanation:
A Gaussian mixture model represents a probability distribution as a weighted sum of multiple Gaussian components. TheMLEmethod can be applied to estimate the parameters of these components (means, variances, and mixing coefficients) by maximizing the likelihood of the observed data. The Expectation- Maximization (EM) algorithm is typically used to perform MLE in GMMs because it can handle hidden (latent) variables representing the component assignments.
Exact Extract from HCIP-AI EI Developer V2.5:
"MLE, implemented through the EM algorithm, is commonly used to estimate the parameters of Gaussian mixture models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Gaussian Mixture Models

NEW QUESTION # 22
......

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