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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q26-Q31):

NEW QUESTION # 26
Maximum likelihood estimation (MLE) requires knowledge of the sample data's distribution type.

A. TRUE
B. FALSE

Answer: A

Explanation:
Maximum likelihood estimation is a statistical method for estimating parameters of a probability distribution by maximizing the likelihood function. To apply MLE, theform of the probability distribution(e.g., normal, exponential) must be known in advance because the likelihood function is defined based on this distribution.
Without knowing the distribution type, the estimation process cannot be properly formulated.
Exact Extract from HCIP-AI EI Developer V2.5:
"MLE assumes that the underlying probability distribution type of the sample data is known and uses it to construct the likelihood function for parameter estimation." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Statistical Parameter Estimation

NEW QUESTION # 27
The basic operations of morphological processing include dilation and erosion. These operations can be combined to achieve practical algorithms such as opening and closing operations.

A. TRUE
B. FALSE

Answer: A

Explanation:
Morphological processing in image analysis is used to process binary or grayscale images based on shape.
* Dilation:Expands object boundaries, useful for filling small holes.
* Erosion:Shrinks object boundaries, useful for removing noise.By combining them:
* Opening:Erosion followed by dilation (removes small objects/noise).
* Closing:Dilation followed by erosion (fills small holes).
Exact Extract from HCIP-AI EI Developer V2.5:
"Morphological processing is based on dilation and erosion. Opening and closing are composite operations derived from these two to handle noise removal and hole filling." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Morphological Image Processing

NEW QUESTION # 28
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 # 29
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 slides over the input feature map using a convolution kernel of a fixed size to extract local features without explicitly defining their features.
B. 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.
C. In the convolutional layer, each neuron only collects some information. This effectively reduces the memory required.
D. 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.

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 # 30
The development of large models should comply with ethical principles to ensure the legal, fair, and transparent use of data.

A. TRUE
B. FALSE

Answer: A

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 # 31
......

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H13-321_V2.5 Exam Bootcamp & H13-321_V2.5 Dumps Torrent & H13-321_V2.5 Exam Simulation

Quiz Huawei - H13-321_V2.5 - HCIP-AI-EI Developer V2.5 –High Pass-Rate Reliable Practice Questions

Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q26-Q31):

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