Most Recent Huawei H13-311_V3.5 Exam Questions & Answers

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.

Machine learning is a rapidly evolving field, and its scope indeed changes over time. With advancements in computational power, the introduction of new algorithms, frameworks, and techniques, and the growing availability of data, the capabilities of machine learning have expanded significantly. Initially, machine learning was limited to simpler algorithms like linear regression, decision trees, and k-nearest neighbors. Over time, however, more complex approaches such as deep learning and reinforcement learning have emerged, dramatically increasing the applications and effectiveness of machine learning solutions.

In the Huawei HCIA-AI curriculum, it is emphasized that AI, especially machine learning, has become more powerful due to these continuous developments, allowing it to be applied to broader and more complex problems. The framework and methodologies in machine learning have evolved, making it possible to perform more sophisticated tasks such as real-time decision-making, image recognition, natural language processing, and even autonomous driving.

As technology advances, the scope of machine learning will continue to shift, providing new opportunities for innovation. This is why it is important to stay updated on recent developments to fully leverage machine learning in various AI applications.

In machine learning:

The testing set is a dataset used after training to evaluate the model's performance and generalization ability. Each sample in this set is called a test sample.

A dataset generally has multiple dimensions, with each dimension representing a feature or attribute of the data.

A typical machine learning process divides the data into a training set (to train the model), a validation set (to tune hyperparameters and avoid overfitting), and a test set (to evaluate the model's final performance).

The statement that the validation set and test set are the same is false because they serve different purposes: validation is for hyperparameter tuning, while testing is for final model evaluation.

AI is not limited to just computer vision and speech processing. In addition to these fields, AI encompasses other important areas such as natural language processing (NLP), robotics, smart finance, autonomous driving, and more. Natural language processing focuses on understanding and generating human language, while other fields apply AI to various industries and applications such as healthcare, finance, and manufacturing. AI is a broad field with numerous application areas.

The process of constructing a knowledge graph typically involves several key steps:

A . Determining the target domain of the knowledge graph: This defines the scope and boundaries of the information to be represented.

B . Data acquisition: Involves gathering structured and unstructured data from various sources.

D . Knowledge fusion: This step involves integrating and reconciling data from multiple sources to create a consistent and coherent knowledge graph.

Creating new concepts is not typically part of the knowledge graph construction process. Instead, knowledge graphs usually focus on extracting, integrating, and structuring existing knowledge, not creating new concepts.

HCIA AI

AI Development Framework: Describes the steps in constructing knowledge graphs, from data acquisition to knowledge fusion and domain determination.