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

The general quantum algorithms include:

A. HHL algorithm (Harrow-Hassidim-Lloyd): An algorithm designed for solving systems of linear equations using quantum computers.

B. Shor algorithm: A quantum algorithm for factoring large integers efficiently, which is important in cryptography.

C. Grover algorithm: A quantum search algorithm used for unstructured database search, providing a quadratic speedup over classical search algorithms.

The A search algorithm* is not a quantum algorithm; it is a classical algorithm used for finding the shortest path in a graph. Therefore, D is incorrect.

HCIA AI References:

Cutting-edge AI Applications: Discusses the potential of quantum algorithms in AI and other advanced computing applications.

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.

Huawei Cloud EI (Enterprise Intelligence) provides a variety of AI services and platforms, including knowledge graph, OCR (Optical Character Recognition), machine translation, and the Celia virtual assistant development platform. These services enable businesses to integrate AI capabilities such as language processing, image recognition, and virtual assistant development into their systems.

Huawei HMS Core (Huawei Mobile Services Core) provides a variety of AI capabilities, including:

HiAI Foundation: Offers basic AI infrastructure, enabling AI computing capabilities.

HiAI Engine: Provides pre-built AI engines for tasks like image processing and NLP.

ML Kit: Provides machine learning features for developers to integrate into apps.

MindSpore Lite is not part of HMS Core but rather a lightweight version of the MindSpore framework designed for mobile and edge devices.

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.

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.

The mean squared error (MSE) loss function is primarily used for regression problems, where the goal is to minimize the difference between the predicted and actual continuous values. For classification problems, where the target output is categorical (e.g., binary or multi-class labels), loss functions like cross-entropy are more suitable, as they are designed to handle the probabilistic interpretation of outputs in classification tasks.

Using MSE for classification could lead to inefficient training because it doesn't capture the probabilistic relationships required for classification tasks.

In machine learning, historical data is crucial for model training and prediction. The model learns from this data, identifying patterns and relationships between features and target variables. While the training algorithm is necessary for defining how the model learns, the input required for the model is historical data, as it serves as the foundation for training the model to make future predictions.

Neural networks and training algorithms are parts of the model development process, but they are not the actual input for model training.

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.

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.