Best Huawei H13-311_V3.5 Exam Practice Material Updated on Jul 26, 2025
New H13-311_V3.5 Actual Exam Dumps, Huawei Practice Test
The Huawei H13-311_V3.5 exam consists of multiple-choice questions that cover various topics related to AI technologies. H13-311_V3.5 exam is conducted online, and candidates have 90 minutes to complete the exam. The passing score is 600 out of 1000. Candidates who pass the exam will be awarded the Huawei Certified ICT Associate-Artificial Intelligence (HCIA-AI) certification, which is valid for three years.
NEW QUESTION # 173
Which of the following about the description of expectations and variances is incorrect?
- A. Expectation reflects the average level of random variable values
- B. Expectation and variance are both numerical characteristics of random variables
- C. The variance reflects the degree of deviation between the random variable and its mathematical expectation
- D. The greater the expectation the smaller the variance
Answer: D
NEW QUESTION # 174
Voice recognition refers to the recognition of audio data as text data.
- A. FALSE
- B. TRUE
Answer: B
NEW QUESTION # 175
A scalar k is multiplied by matrix A equal to k and each of the numbers in A is multiplied.
- A. True
- B. False
Answer: A
NEW QUESTION # 176
Sigmoid, tanh, and softsign activation functions cannot avoid vanishing gradient problems when the network is deep.
- A. FALSE
- B. TRUE
Answer: B
Explanation:
Activation functions like Sigmoid, tanh, and softsign suffer from the vanishing gradient problem when used in deep networks. This happens because, in these functions, gradients become very small as the input moves away from the origin (either positively or negatively). As a result, the weights of the earlier layers in the network receive very small updates, hindering the learning process in deep networks. This is one reason why activation functions like ReLU, which avoid this issue, are often preferred in deep learning.
NEW QUESTION # 177
All kernels of the same convolutional layer in a convolutional neural network share a weight.
- A. TRUE
- B. FALSE
Answer: B
Explanation:
In a convolutional neural network (CNN), each kernel (also called a filter) in the same convolutional layer does not share weights with other kernels. Each kernel is independent and learns different weights during training to detect different features in the input data. For instance, one kernel might learn to detect edges, while another might detect textures.
However, the same kernel's weights are shared across all spatial positions it moves across the input feature map. This concept of weight sharing is what makes CNNs efficient and well-suited for tasks like image recognition.
Thus, the statement that all kernels share weights is false.
HCIA AI
Reference:
Deep Learning Overview: Detailed description of CNNs, focusing on kernel operations and weight sharing mechanisms within a single kernel, but not across different kernels.
NEW QUESTION # 178
Which of the following are use cases of generative adversarial networks?
- A. Photo repair
- B. Generating face images
- C. Generating images from text
- D. Generating a 3D model from a 2D image
Answer: A,B,C,D
Explanation:
Generative Adversarial Networks (GANs) are widely used in several creative and image generation tasks, including:
A . Photo repair: GANs can be used to restore missing or damaged parts of images.
B . Generating face images: GANs are known for their ability to generate realistic face images.
C . Generating a 3D model from a 2D image: GANs can be used in applications where 2D images are converted into 3D models.
D . Generating images from text: GANs can also generate images based on text descriptions, as seen in tasks like text-to-image synthesis.
All of the provided options are valid use cases of GANs.
HCIA AI
Reference:
Deep Learning Overview: Discusses the architecture and use cases of GANs, including applications in image generation and creative content.
AI Development Framework: Covers the role of GANs in various generative tasks across industries.
NEW QUESTION # 179
The activation function plays an important role in the neural network model learning and understanding of very complex problems. The following statement about the activation function is correct.
- A. The activation function is partly a nonlinear function, partly a linear function
- B. Activation functions are non-linear functions
- C. Activation functions are linear functions
- D. Most of the activation functions are nonlinear functions, and a few are linear functions
Answer: B
NEW QUESTION # 180
Huawei's full-stack AI solution includes Ascend, MindSpore, and ModelArts. (Enter an acronym.)
- A. AIIS
- B. CANN
- C. AII
- D. None of the above
Answer: B
Explanation:
CANN (Compute Architecture for Neural Networks) is part of Huawei's full-stack AI solution, which includes Ascend (hardware), MindSpore (AI framework), and ModelArts (AI development platform). CANN optimizes the computing efficiency of AI models and provides basic software components for the Ascend AI processors. This architecture supports deep learning and machine learning tasks by enhancing computational performance and providing better neural network training efficiency.
Together, Ascend, MindSpore, and CANN form a critical infrastructure that underpins Huawei's AI development ecosystem, allowing seamless integration from hardware to software.
NEW QUESTION # 181
Tensorflow Operations and Computation Graph are not - run in the Session
- A. False
- B. True
Answer: A
NEW QUESTION # 182
Ce11 Provides basic modules for defining and performing calculations, Ce11 The object can be executed directly, the following statement is wrong?
- A. bprop (Optional), The reverse of the custom module
- B. Construct, Define the execution process. In graph mode, it will be compiled into graphs for execution, and there is no syntax restriction
- C. There's some left optim Commonly used optimizers,wrap Pre-defined commonly used network packaging functions Ce11
- D. __init__,Initialization parameters(Parameter), Submodule(Ce11),operator(Primitive)Equal group Software for initial verification
Answer: A,C,D
NEW QUESTION # 183
The timestamps in the Python language- are represented by how long (in seconds) elapsed from midnight (epoch) on January 1, 1970
- A. True
- B. False
Answer: A
NEW QUESTION # 184
In a neural network, knowing the weight and deviations of each neuron is the most important step. If you know the exact weights and deviations of neurons in some way, you can approximate any function What is the best way to achieve this?
- A. The above is not correct
- B. Random assignment, pray that they are correct
- C. Assign an initial value to iteratively update weight by checking the difference between the best value and the initial
- D. Search for a combmat1on of weight and deviation until the best value 1s obtained
Answer: C
NEW QUESTION # 185
Functions are well-organized, non-reusable code segments used to implement a single, or associated Function.
- A. False
- B. True
Answer: A
NEW QUESTION # 186
"AI application fields include only computer vision and speech processing." Which of the following is true about this statement?
- A. This statement is true. Computer vision is the most important AI application.
- B. This statement is false. The application fields of AI include computer vision, speech processing, natural language processing, and others.
- C. This statement is true. Voice data is processed with extremely high accuracy.
- D. This statement is false. AI application fields include only computer vision and natural language processing.
Answer: B
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.
NEW QUESTION # 187
The following statement about recurrent neural networks is wrong?
- A. Recurrent neural network can be abbreviated as RNN
- B. Recurrent neural network can be unfolded according to the time axis
- C. LSTM It is also a recurrent neural network
- D. LSTM Unable to solve the problem of vanishing gradient
Answer: D
NEW QUESTION # 188
Which command can be checked Atlas 300 (3000) Whether the accelerator card is in place?
- A. 1spci grep'atlas'
- B. 1spci | grep'd100'
- C. atlas info
- D. 1spci | grep'npu'
Answer: B
NEW QUESTION # 189
In the process of training the neural network, our goal is to keep the loss function reduced. Which of the following methods do we usually use to minimize the loss function?
- A. Regularization
- B. Cross-validation
- C. Dropout
- D. Gradient descent
Answer: D
NEW QUESTION # 190
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Huawei H13-311_V3.5 (HCIA-AI V3.5) Exam is a certification exam designed for IT professionals looking to validate their skills and knowledge in the field of Artificial Intelligence. H13-311_V3.5 exam focuses on key AI technologies, including machine learning, natural language processing, and computer vision. By passing H13-311_V3.5 exam, candidates can demonstrate their ability to design and implement AI solutions using Huawei's AI technologies.
Passing the Huawei H13-311_V3.5 exam is a great way to demonstrate your knowledge and skills in the field of AI and ML. HCIA-AI V3.5 certification is highly regarded by employers in the IT industry, and it can help you to advance your career and increase your earning potential. With the growing demand for AI and ML professionals in the IT industry, obtaining the HCIA-AI V3.5 certification can be a valuable asset to your career.
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