Pass the Huawei HCIA-AI H13-311_V3.5 Questions and answers with CertsForce

The pad_mode parameter in mindspore.nn.Conv2d() can take values such as:

same: Ensures the output has the same spatial dimensions as the input.

valid: Performs convolution without padding, resulting in an output smaller than the input.

Other values like "pad" and "nopadding" are not valid options for the pad_mode parameter.

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.

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 References:

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.

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.

The order of a tensor refers to its rank, which is the number of dimensions it has. For the tensor [[0,1],[2,3]], the rank is 2 because it is a 2x2 matrix, meaning it has 2 dimensions.

The nn module in MindSpore provides essential tools for building neural networks, including:

C. Optimizers: such as Momentum and Adam, which are used to adjust the weights of the model during training.

D. Loss functions: such as MSELoss (Mean Squared Error Loss) and SoftmaxCrossEntropyWithLogits, which are used to compute the difference between predicted and actual values.

The other options are incorrect because:

A. Hyperparameter search modes (like GridSearch and RandomSearch) are typically found in model training and tuning modules, but not in the nn module.

B. Model evaluation indicators like F1 Score and AUC are also handled by specific evaluation functions or libraries outside the nn module.

HCIA AI References:

AI Development Framework: Detailed coverage of MindSpore’s nn module, its optimizers, and loss functions.

Introduction to Huawei AI Platforms: Explains various MindSpore features, including network construction and training.

HarmonyOS provides AI capabilities not only through HMS Core (Huawei Mobile Services Core), but also through other system-level integrations and AI frameworks. While HMS Core is one way to offer AI functionalities, HarmonyOS also has native support for AI processing that can be accessed by external systems or applications beyond HMS Core.

Thus, the statement is false as AI capabilities are not limited solely to HMS Core in HarmonyOS.

HCIA AI References:

Introduction to Huawei AI Platforms: Covers HarmonyOS and the various ways it integrates AI capabilities into external systems.

Huawei Cloud ModelArts provides ModelBox, a tool for device-edge-cloud joint development, enabling efficient deployment across multiple environments. Some of its key optimization policies include:

Hardware affinity: Ensures that the models are optimized to run efficiently on the target hardware.

Operator optimization: Improves the performance of AI operators for better model execution.

Automatic segmentation of operators: Automatically segments operators for optimized distribution across devices, edges, and clouds.

Model replication is not an optimization policy offered by ModelBox.

When building a decision tree, the steps generally involve:

Decision tree generation: This is the process where the model iteratively splits the data based on feature values to form branches.

Pruning: This step occurs post-generation, where unnecessary branches are removed to reduce overfitting and enhance generalization.

Feature selection: This is part of decision tree construction, where relevant features are selected at each node to determine how the tree branches.

Data cleansing, on the other hand, is a preprocessing step carried out before any model training begins. It involves handling missing or erroneous data to improve the quality of the dataset but is not part of the decision tree building process itself.

HCIA AI References:

Machine Learning Overview: Includes a discussion on decision tree algorithms and the process of building decision trees.

AI Development Framework: Highlights the steps for building machine learning models, separating data preprocessing (e.g., data cleansing) from model building steps.

Logistic regression is not a solution for underfitting in regression models, as it is used primarily for classification problems rather than regression tasks. If underfitting occurs, it means that the model is too simple to capture the underlying patterns in the data. Solutions include using a more complex regression model like polynomial regression or increasing the number of features in the dataset.

Other options like adding a regularization term for overfitting (Lasso or Ridge) and using data cleansing and feature engineering are correct methods for improving model performance.