H13-311_V3.5 Dumps with Practice Exam Questions Answers
H13-311_V3.5 by HCIA-AI Actual Free Exam Practice Test
NEW QUESTION # 237
The face detection service will report an error when there is no face in the test image.
- A. FALSE
- B. TRUE
Answer: A
NEW QUESTION # 238
The following about the general form identification service returned type The field statement is correct?
- A. type for table Time represents the form recognition area
- B. type for text Time represents the text recognition area
- C. type Representative form type
- D. type Representative text recognition area type
Answer: A,B
NEW QUESTION # 239
The derivative of the Rectified Linear Unit (ReLU) activation function in the positive interval is always:
- A. Variable
- B. 0.5
- C. 0
- D. 1
Answer: C
Explanation:
The Rectified Linear Unit (ReLU) activation function is defined as f(x)=max(0,x)f(x) = \max(0, x)f(x)=max(0,x). In the positive interval, where x>0x > 0x>0, the derivative of ReLU is always 1. This makes ReLU popular for deep learning networks because it helps avoid the vanishing gradient problem during backpropagation, ensuring efficient gradient flow.
NEW QUESTION # 240
The following options belong to keras under estimator
The encapsulation methods are?
- A. Output model
- B. prediction
- C. training
- D. Assessment
Answer: B,C,D
NEW QUESTION # 241
Not supported in the following options TensorFLow2.0 The attributes for dimensional transformation are.
- A. squeeze
- B. reshape
- C. gather
- D. transpose
Answer: C
NEW QUESTION # 242
In random forest, what strategy is used to determine the outcome of the final ensemble model?
- A. Find the average
- B. Voting system
- C. Cumulative system
- D. Cumulative system
Answer: A,B
NEW QUESTION # 243
In neural networks, weights are often shareD.
Which of the following neural networks will share weights?
- A. sensor
- B. Recurrent neural network
- C. Fully connected neural network
- D. Convolutional Neural Network
Answer: B,D
NEW QUESTION # 244
Match the input and output of a generative adversarial network (GAN).
Answer:
Explanation:
NEW QUESTION # 245
Which of the following are callback options provided by MindSpore?
- A. LossMonitor
- B. SummaryCollector
- C. ModelCheckpoint
- D. TrainStep
Answer: A,B,C
Explanation:
MindSpore provides several callback functions that can be used to monitor, modify, or control the behavior of the training process. These include:
SummaryCollector: Collects summaries such as loss and accuracy for visualization and monitoring.
ModelCheckpoint: Saves model parameters during or after training.
LossMonitor: Monitors the loss values during training and can stop training if certain conditions are met.
TrainStep is not a callback but rather a fundamental step in training.
NEW QUESTION # 246
Which is not a deep learning algorithm?
- A. Convolutional neural networks
- B. Setr-encoder
- C. Recurrent neural networks
- D. Support vector machine
Answer: D
NEW QUESTION # 247
In neural networks, which of the following methods are used to update the parameters when training the network to minimize the loss function?
- A. Backpropagation algorithm
- B. Convolution calculation
- C. Pooling calculation
- D. Forward propagation algorithm
Answer: A
NEW QUESTION # 248
The global gradient descent, stochastic gradient descent, and batch gradient descent algorithms are gradient descent algorithms. Which of the following is true about these algorithms?
- A. The stochastic gradient algorithm can find the minimum value of the loss function.
- B. The global gradient algorithm can find the minimum value of the loss function.
- C. The convergence process of the global gradient algorithm is time-consuming.
- D. The batch gradient algorithm can solve the problem of local minimum value.
Answer: C
Explanation:
The global gradient descent algorithm evaluates the gradient over the entire dataset before each update, leading to accurate but slow convergence, especially for large datasets. In contrast, stochastic gradient descent updates the model parameters more frequently, which allows for faster convergence but with noisier updates. While batch gradient descent updates the parameters based on smaller batches of data, none of these algorithms can fully guarantee finding the global minimum in non-convex problems, where local minima may exist.
NEW QUESTION # 249
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 linear functions
- C. Most of the activation functions are nonlinear functions, and a few are linear functions
- D. Activation functions are non-linear functions
Answer: D
NEW QUESTION # 250
The optimizer is an important part of training neural networks. The purpose of using the optimizer does not include which of the following:
- A. Avoid overfitting
- B. Reduce the difficulty of manual parameter setting
- C. Avoid local extremes
- D. Speed up algorithm convergence
Answer: A
NEW QUESTION # 251
Which of the following are AI Application areas?
- A. Wisdom education
- B. Smart home
- C. Smart City
- D. Smart medical
Answer: A,B,C,D
NEW QUESTION # 252
Which of the following about the description of expectations and variances is incorrect?
- A. Expectation reflects the average level of random variable values
- B. The variance reflects the degree of deviation between the random variable and its mathematical expectation
- C. The greater the expectation the smaller the variance
- D. Expectation and variance are both numerical characteristics of random variables
Answer: C
NEW QUESTION # 253
When using the following code to construct a neural network, MindSpore can inherit the Cell class and rewrite the __init__ and construct methods.
- A. FALSE
- B. TRUE
Answer: B
Explanation:
In MindSpore, the neural network structure is defined by inheriting the Cell class, which represents a computational node or a layer in the network. Users can customize the network by overriding the __init__ method (for initializing layers) and the construct method (for defining the forward pass of the network). This modular design allows for easy and flexible neural network construction.
Thus, the statement is true because MindSpore's framework allows developers to build neural networks by extending the Cell class and defining custom behavior through the __init__ and construct methods.
HCIA AI
Reference:
AI Development Framework: Detailed coverage of building neural networks in MindSpore, including how to inherit from the Cell class and rewrite key methods for custom network architecture.
NEW QUESTION # 254
Which of the following are common gradient descent methods?
- A. Multi-dimensional gradient descent (MDGD)
- B. Stochastic gradient descent (SGD)
- C. Mini-batch gradient descent (MBGD)
- D. Batch gradient descent (BGD)
Answer: B,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 # 255
The following does not belong TensorFlow2.0 Is characterized by?
- A. multi-language
- B. Multi-platform
- C. Multi-core CPU accelerate
- D. distributed
Answer: C
NEW QUESTION # 256
What people say about neural networks is wrong?
- A. There is no connection between neurons in the same layer of the feedforward neural network
- B. As the number of hidden layers of the neural network increases, the classification ability of the model gradually weakens
- C. The limitation of a single-layer perceptron is that it cannot solve the XOR problem
- D. The feedforward neural network can be represented by a directed acyclic graph
Answer: B
NEW QUESTION # 257
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