H13-311_V3.5 Exam Questions and Answers

QUESTION: 6

What are the application scenarios of computer vision?

Video action analysis
Image search
Smart albums
Voice navigation

Answer(s): A,B,C

Explanation:

Computer vision, a subfield of AI, has various application scenarios that involve the analysis and understanding of images and videos. Some key application scenarios include:
Video action analysis: Identifying and analyzing human actions or movements in videos.

Image search: Using visual information to search for similar images in large databases. Smart albums: Organizing and categorizing photos using AI-based image recognition algorithms to group them by themes, people, or events.
Voice navigation is a part of natural language processing and speech recognition, not computer vision.

Reference:

Huawei HCIA-AI Certification, AI Applications in Computer Vision.

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QUESTION: 7

Which of the following is NOT a commonly used AI computing framework?

PyTorch
MindSpore
TensorFlow
OpenCV

Answer(s): D

Explanation:

OpenCV is a library used primarily for computer vision tasks like image and video processing. It is not considered an AI computing framework in the same way as PyTorch, MindSpore, or TensorFlow, which are commonly used frameworks for developing AI and machine learning models. AI frameworks like PyTorch, TensorFlow, and Huawei's MindSpore are designed to facilitate the development and deployment of deep learning models.

Reference:

Huawei HCIA-AI Certification, AI Development Frameworks.

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QUESTION: 8

"Today's speech processing technology can achieve a recognition accuracy of over 90% in any case." Which of the following is true about this statement?

This statement is incorrect. The accuracy of speech recognition is high, but not extremely high.
This statement is incorrect. In many situations, noise and background sound have a huge impact on speech recognition accuracy.
This statement is correct. Speech processing can achieve a high level of accuracy.
This statement is correct. Speech processing has a long history and the technology is very mature.

Answer(s): B

Explanation:

While speech recognition technology has improved significantly, its accuracy can still be affected by external factors such as noise, background sound, accents, and speech clarity. Although systems can achieve over 90% accuracy under controlled conditions, the accuracy drops in noisy or complex real- world environments. Therefore, the statement that today's speech processing technology can always achieve high recognition accuracy is incorrect.
Speech recognition systems are sophisticated but still face challenges in environments with heavy noise, where the technology has difficulty interpreting speech accurately.

Reference:

Huawei HCIA-AI Certification, AI Applications in Speech Processing.

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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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Huawei H13-311_V3.5 Exam (page: 2) Huawei HCIA-AI V3.5 Updated on: 15-Dec-2025

QUESTION: 6

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QUESTION: 7

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QUESTION: 8

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QUESTION: 9

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QUESTION: 10

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Huawei H13-311_V3.5 Exam (page: 2)
Huawei HCIA-AI V3.5
Updated on: 15-Dec-2025