QUESTION: 9

"AI application fields include only computer vision and speech processing." Which of the following is true about this statement?

This statement is false. The application fields of AI include computer vision, speech processing, natural language processing, and others.
This statement is false. AI application fields include only computer vision and natural language processing.
This statement is true. Voice data is processed with extremely high accuracy.
This statement is true. Computer vision is the most important AI application.

Answer(s): A

Explanation:

AI is not limited to just computer vision and speech processing. In addition to these fields, AI encompasses other important areas such as natural language processing (NLP), robotics, smart finance, autonomous driving, and more. Natural language processing focuses on understanding and generating human language, while other fields apply AI to various industries and applications such as healthcare, finance, and manufacturing. AI is a broad field with numerous application areas.

Reference:

Huawei HCIA-AI Certification, AI Overview and Applications.

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QUESTION: 10

Which of the following are common gradient descent methods?

Batch gradient descent (BGD)
Mini-batch gradient descent (MBGD)
Multi-dimensional gradient descent (MDGD)
Stochastic gradient descent (SGD)

Answer(s): A,B,D

Explanation:

The gradient descent method is a core optimization technique in machine learning, particularly for neural networks and deep learning models. The common gradient descent methods include:
Batch Gradient Descent (BGD): Updates the model parameters after computing the gradients from the entire dataset.
Mini-batch Gradient Descent (MBGD): Updates the model parameters using a small batch of data, combining the benefits of both batch and stochastic gradient descent. Stochastic Gradient Descent (SGD): Updates the model parameters for each individual data point, leading to faster but noisier updates.
Multi-dimensional gradient descent is not a recognized method in AI or machine learning.

Reference:

Huawei HCIA-AI Certification, Machine Learning Algorithms.

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QUESTION: 11

Which of the following algorithms presents the most chaotic landscape on the loss surface?

Stochastic gradient descent
MGD
MBGD
BGD

Answer(s): A

Explanation:

Stochastic Gradient Descent (SGD) presents the most chaotic landscape on the loss surface because it updates the model parameters for each individual training example, which can introduce a significant amount of noise into the optimization process. This leads to a less smooth and more chaotic path toward the global minimum compared to methods like batch gradient descent or mini-batch gradient descent, which provide more stable updates.

Reference:

Huawei HCIA-AI Certification, Machine Learning Algorithms.

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QUESTION: 12

Which of the following statements are true about the k-nearest neighbors (k-NN) algorithm?

k-NN typically uses the mean value method to predict regression.
k-NN typically uses the majority voting method to predict classification.
k-NN is a parametric method often used for datasets with regular decision boundaries.
The k-NN algorithm determines which class an object belongs to based on the class to which most of the object's k nearest neighbors belong.

Answer(s): B,D

Explanation:

The k-nearest neighbors (k-NN) algorithm is a non-parametric algorithm used for both classification and regression. In classification tasks, it typically uses majority voting to assign a label to a new instance based on the most common class among its nearest neighbors. The algorithm works by calculating the distance (often using Euclidean distance) between the query point and the points in the dataset, and then assigning the query point to the class that is most frequent among its k nearest neighbors.
For regression tasks, k-NN can predict the outcome based on the mean of the values of the k nearest neighbors, although this is less common than its classification use.

Reference:

Huawei HCIA-AI Certification, Machine Learning Overview.

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