H13-311_V3.5 Dumps 2025 – New Huawei H13-311_V3.5 Exam Questions [Q10-Q34]

admin • August 6, 2025

H13-311_V3.5 Dumps 2025 – New Huawei H13-311_V3.5 Exam Questions [Q10-Q34]

Rate this post

H13-311_V3.5 Dumps 2025 – New Huawei H13-311_V3.5 Exam Questions

Free H13-311_V3.5 Braindumps Download Updated on Aug 06, 2025 with 430 Questions

NEW QUESTION 10
In the environment preparation for invoking HUAWEI CLOUD services, which of the following does not require confirmation of installation?

requests

setuptools

websocket-client

Jieba

NEW QUESTION 11
Ascend among the following options 310 And Shengteng 910 The attributes are the same?

Maximum power

Architecture

Integer precision

7nm Process

NEW QUESTION 12
The following description of machine learning is correct?

Deep learning is a branch of machine learning

Deep learning and machine learning are mutually inclusive

Deep learning and machine learning are both artificial intelligence but have no relationship to each other

None of the above

NEW QUESTION 13
If the deep learning neural network has the problem of gradient disappearance or gradient explosion, our common solution is.

Gradient shear

Random undersampling

use Relu Activation function

Regularization

NEW QUESTION 14
What is the most important difference between batch gradient descent, mini-batch gradient descent, and stochastic gradient descent?

Gradient size

Gradient direction

Learning rate

Number of samples used

NEW QUESTION 15
The main computing resources included in the Da Vinci architecture computing unit are?

Vector calculation unit

Scalar Computing Unit

Tensor computing unit

Matrix calculation unit

NEW QUESTION 16
X, Y are random variables, C is a constant, the nature of the difference in the following options which is wrong? (Multiple choice)

D(C)=O

D(X+ Y)=D(X)+D(Y)

D(CX)=C’C’D()()

D(XY)=D(X)D{Y)

NEW QUESTION 17
All kernels of the same convolutional layer in a convolutional neural network share a weight.

TRUE

FALSE

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 18
Computer vision is the study of how to make computers “see”.

TRUE

FALSE

NEW QUESTION 19
The following belongs to TensorFlow2.0 The characteristic is?

Introduce Keras interface

Support static image mechanism

Support more platforms and more languages

Continue to be compatible Tensorflowl.x Related modules

NEW QUESTION 20
On-Device Execution, that is, the entire image is offloaded and executed, and the computing power of the Yiteng chip can be fully utilized, which can greatly reduce the interaction overhead, thereby increasing the accelerator occupancy rate.
On-Device The following description is wrong?

MindSpore Realize decentralized autonomy through adaptive graph optimization driven by gradient data A11 Reduce, Gradient aggregation is in step, and calculation and communication are fully streamlined

Challenges of model execution under super chip computing power: Memory wall problems, high interaction overhead, and difficulty in data supply. Partly in Host Executed, partly in Device Execution, interaction overhead is even much greater than execution overhead, resulting in low accelerator occupancy

MindSpore Through the chip-oriented depth map optimization technology, the synchronization wait is less, and the “data computing communication” is maximized. The parallelism of “trust”, compared with training performance Host Side view scheduling method is flat

The challenge of distributed gradient aggregation under super chip computing power:ReslNet50 Single iteration 20ms Time will be generated The synchronization overhead of heart control and the communication overhead of frequent synchronization. Traditional methods require 3 Synchronization completed A11 Reduce, Data-driven method autonomy A11 Reduce, No control overhead

NEW QUESTION 21
AI inference chips need to be optimized and are thus more complex than those used for training.

TRUE

FALSE

AI inference chips are generally simpler than training chips because inference involves running a trained model on new data, which requires fewer computations compared to the training phase. Training chips need to perform more complex tasks like backpropagation, gradient calculations, and frequent parameter updates. Inference, on the other hand, mostly involves forward pass computations, making inference chips optimized for speed and efficiency but not necessarily more complex than training chips.
Thus, the statement is false because inference chips are optimized for simpler tasks compared to training chips.
HCIA AI
Reference:
Cutting-edge AI Applications: Describes the difference between AI inference and training chips, focusing on their respective optimizations.
Deep Learning Overview: Explains the distinction between the processes of training and inference, and how hardware is optimized accordingly.

NEW QUESTION 22
Which of the following are the characteristics of the Python language? {Multiple choice)

Explanatory

Process oriented

Object-oriented

Dynamic data type

NEW QUESTION 23
Image label service returned tag There can be more than one.

TRUE

FALSE

NEW QUESTION 24
In a fully-connected structure, a hidden layer with 1000 neurons is used to process an image with the resolution of 100 x 100. Which of the following is the correct number of parameters?

100,000

10,000

1,000,000

10,000,000

In a fully-connected layer, the number of parameters is calculated by multiplying the number of input features by the number of neurons in the hidden layer. For an image of resolution 100×100=10,000100 \times 100 = 10,000100×100=10,000 pixels and a hidden layer of 1,000 neurons, the total number of parameters is 10,000×1,000=1,000,00010,000 \times 1,000 = 1,000,00010,000×1,000=1,000,000.

NEW QUESTION 25
Traffic agent can achieve () Hour full time/Global traffic awareness?

7?4

5?4

5?2

7?2

NEW QUESTION 26
Tensorflow Operations and Computation Graph are not – run in the Session

True

False

NEW QUESTION 27
Which is wrong with the Python module time introduction method?

import time

from time import *

import time as t

from time

NEW QUESTION 28
What are the characteristics of Python code? (Multiple Choice)

leg1bil1ty

Simpl1c1ty

Rapidity

Scalab1hty

NEW QUESTION 29
With a lot of sales data but no labels, companies want to identify VIP Customer, the following model Suitable?

Logistic regression

SVM

K-Means

Hierarchical clustering

NEW QUESTION 30
Generative confrontation networks are currently widely used.
The following scenarios can use this network?

Data enhancement

Semantic segmentation

Information retrieval

Image generation

NEW QUESTION 31
An algorithm of unsupervised learning classifies samples in a dataset into several categories. Samples belonging to the same category have high similarity.

TRUE

FALSE

In unsupervised learning, the goal is to find hidden patterns or intrinsic structures in input data without labeled outcomes. One common unsupervised learning task is clustering, where an algorithm groups the dataset into several categories or clusters. Samples within the same cluster have high similarity based on certain features, while samples in different clusters have low similarity. Examples of clustering algorithms include k-means and hierarchical clustering.

NEW QUESTION 32
What people say about neural networks is wrong?

As the number of hidden layers of the neural network increases, the classification ability of the model gradually weakens

The limitation of a single-layer perceptron is that it cannot solve the XOR problem

The feedforward neural network can be represented by a directed acyclic graph

There is no connection between neurons in the same layer of the feedforward neural network

NEW QUESTION 33
What are the steps that are not part of the operation of a Python file object?

open

delete

read

write

NEW QUESTION 34
When dealing with actual problems, when should machine learning be used in the following situations?

The data distribution itself changes over time, requiring continuous re-adaptation of the program, such as predicting the trend of merchandise sales

The complexity of the rules is low, and the problem is small

Task rules will change over time, such as defect detection on the production line

The rules are very complicated or cannot be described, such as face recognition and voice recognition

Loading …

Huawei H13-311_V3.5 Exam Practice Test Questions: https://www.2pass4sure.com/HCIA-AI/H13-311_V3.5-actual-exam-braindumps.html

The New C-BCBDC-2505 2025 Updated Verified Study Guides & Best Courses [Q26-Q43]

[Aug 08, 2025] Fully Updated Free Actual CompTIA N10-008 Exam Questions [Q460-Q480]

H13-311_V3.5 Practice Tests

H13-311_V3.5 Dumps 2025 – New Huawei H13-311_V3.5 Exam Questions [Q10-Q34]

Related Certifications

H13-611_V4.5
H12-311
H12-722
H12-723_V3.0
H19-427_V1.0-ENU
H12-261_V3.0
H12-831-ENU
H12-425_V2.0
H12-821_V1.0-ENU
H19-319_V2.0

H13-311_V3.5 Dumps 2025 – New Huawei H13-311_V3.5 Exam Questions [Q10-Q34]

Leave a Reply Cancel reply