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NEW QUESTION # 141
About the image content review service returned when the call is successful suggestion Field, the correct statement is?
- A. block Representative contains sensitive information and does not pass
- B. review The representative needs a manual review
- C. pass Representative does not contain sensitive information, passed
- D. suggestion The field represents whether the test passed
Answer: A,B,C,D
NEW QUESTION # 142
The Python language can use multiple statements on the same line, separated by commas ".".
- A. False
- B. True
Answer: A
NEW QUESTION # 143
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?
- A. 100,000
- B. 10,000,000
- C. 10,000
- D. 1,000,000
Answer: D
Explanation:
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 # 144
Which of the following are common gradient descent methods?
- A. Stochastic gradient descent (SGD)
- B. Multi-dimensional gradient descent (MDGD)
- C. Mini-batch gradient descent (MBGD)
- D. Batch gradient descent (BGD)
Answer: A,C,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.
NEW QUESTION # 145
Which of the following descriptions about Python program are correct? (Multiple choice)
- A. Expressions create and process objects
- B. The program consists of modules
- C. Module contains statements
- D. statement contains an expression
Answer: A,B,C,D
NEW QUESTION # 146
SVM What are the commonly used kernel functions in?
- A. Polynomial kernel function
- B. Sigmiod Kernel function
- C. Linear kernel
- D. Gaussian kernel function
Answer: A,B,C,D
NEW QUESTION # 147
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 # 148
What quotation marks can the Python language use? (Multiple Choice)
- A. Four quotes
- B. Three quotes
- C. Double quotes
- D. Single quotes
Answer: B,C,D
NEW QUESTION # 149
What is wrong about the image content review service?
- A. terrorism Test results for political violence
- B. confidence Represents confidence, range 0-100
- C. labe1 Label representing each test result
- D. politics Test results for sensitive persons involved in politics
Answer: B
NEW QUESTION # 150
Which of the following statements about supervised learning is correct?
- A. Supervised learning cannot use cross-validation for training
- B. Decision tree is a supervised learning
- C. Supervised learning can be trained without labels
- D. Supervised learning is a rule-based algorithm
Answer: B
NEW QUESTION # 151
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. Random assignment, pray that they are correct
- B. Search for a combmat1on of weight and deviation until the best value 1s obtained
- C. The above is not correct
- D. Assign an initial value to iteratively update weight by checking the difference between the best value and the initial
Answer: D
NEW QUESTION # 152
In a hyperparameter-based search, the hyperparameters of a model are searched based on the data on and the model's performance metrics.
- A. TRUE
- B. FALSE
Answer: A
Explanation:
In machine learning, hyperparameters are the parameters that govern the learning process and are not learned from the data. Hyperparameter optimization or hyperparameter tuning is a critical part of improving a model's performance. The goal of a hyperparameter-based search is to find the set of hyperparameters that maximizes the model's performance on a given dataset.
There are different techniques for hyperparameter tuning, such as grid search, random search, and more advanced methods like Bayesian optimization. The performance of the model is assessed based on evaluation metrics (like accuracy, precision, recall, etc.), and the hyperparameters are adjusted accordingly to achieve the best performance.
In Huawei's HCIA AI curriculum, hyperparameter optimization is discussed in relation to both traditional machine learning models and deep learning frameworks. The course emphasizes the importance of selecting appropriate hyperparameters and demonstrates how frameworks such as TensorFlow and Huawei's ModelArts platform can facilitate hyperparameter searches to optimize models efficiently.
HCIA AI
Reference:
AI Overview and Machine Learning Overview: Emphasize the importance of hyperparameters in model training.
Deep Learning Overview: Highlights the role of hyperparameter tuning in neural network architectures, including tuning learning rates, batch sizes, and other key parameters.
AI Development Frameworks: Discusses the use of hyperparameter search tools in platforms like TensorFlow and Huawei ModelArts.
NEW QUESTION # 153
When we describe the house, we usually use the attributes such as residential area, house type, decoration type, etC. If we use plain With Bayes as a model, we assume that there is no relationship between attributes.
- A. TRUE
- B. FALSE
Answer: A
NEW QUESTION # 154
About Bayesian formula- P(WlX)=P(XlW)*P(W)/P(X) What is the correct description?
- A. P(W) is the posterior probability
- B. P(XIW) 1s a conditional probability
- C. P(X) is the posterior probability
- D. P(WIX) is a prior probability
Answer: B
NEW QUESTION # 155
The number of hidden layers of the deep learning neural network has a certain impact on the performance of the network.
The following statement about its impact is correct.:
- A. The number of hidden layers increases appropriately, the weaker the resolution ability of the neural network
- B. The number of hidden layers is appropriately reduced, and the resolution ability of the neural network remains unchanged
- C. Increase the number of hidden layers appropriately, the stronger the resolution ability of the neural network
- D. The number of hidden layers is appropriately reduced, the stronger the resolution ability of the neural network is
Answer: C
NEW QUESTION # 156
With Atlas300 (3000)Install the accelerator card in the server DDK (Device Development Kit) Only need to install when dependent on the environment Python3.
- A. TRUE
- B. FALSE
Answer: A
NEW QUESTION # 157
What is the subject of artificial intelligence?
- A. Comprehensive mterd1sc1pl1nary and marginal d1sc1phnes
- B. Psychology and Physiology
- C. Mathematics and Physiology
- D. Linguistics
Answer: A
NEW QUESTION # 158
Jobs that are repetitive and require weak social skills are the easiest to be AI Replaced work.
- A. TRUE
- B. FALSE
Answer: A
NEW QUESTION # 159
TensorFlow2.0 middle Keras The three main advantages of the interface are user-friendly, modular and Combination, easy to expand.
- A. TRUE
- B. FALSE
Answer: A
NEW QUESTION # 160
HUAWEI HiAI Which tool does the platform support to integrate with?
- A. Jupyter Notebook
- B. Spider
- C. MyEclipse
- D. Android Studio
Answer: D
NEW QUESTION # 161
When the universal text recognition service is successfully called, the returned text recognition result is stored in which of the following fields?
- A. words
- B. content
- C. result
- D. text
Answer: C
NEW QUESTION # 162
For the image classification problem, which of the following neural networks is more suitable to solve this problem?
- A. Convolutional Neural Network
- B. Fully connected neural network
- C. Recurrent neural network
- D. sensor
Answer: A
NEW QUESTION # 163
Fill in blanks
The general process of building a project using machine learning involves the following steps: split data, _________________ the model, deploy the model the model, and fine-tune the model.
Answer:
Explanation:
train
NEW QUESTION # 164
Which of the following aspects belong to Huawei's full stack AI solution?
- A. ModelArts
- B. Ascend
- C. CANN
- D. MindSpore
Answer: A,B,C,D
NEW QUESTION # 165
TensorFlow2.0 of keras.preprocessing The role is?
- A. keras Model deployment tool
- B. keras Built-in optimizer
- C. Keras Generative model tool
- D. keras Data processing tools
Answer: D
NEW QUESTION # 166
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