Huawei Updated H13-311_V3.5 Exam Questions and Answers by gus

MindSpore, Huawei's AI framework, includes a data processing engine designed to efficiently handle large datasets during model training. The core feature of this engine is the ability to convert training samples into a format called MindRecord, which optimizes data input and output processes for training. This format ensures that the data pipeline is fast and flexible, providing data efficiently to the training network.

The statement is true because one of MindSpore's core functionalities is to preprocess data and optimize its flow into the neural network training pipeline using the MindRecord format.

HCIA AI References:

Introduction to Huawei AI Platforms: Covers MindSpore's architecture, including its data processing engine and the use of the MindRecord format for efficient data management.

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.

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.

The concept of "artificial intelligence" was first formally introduced in 1956 during the Dartmouth Conference, organized by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. This event is widely regarded as the birth of AI as a field of study. The conference aimed to explore the idea that human intelligence could be simulated by machines, laying the groundwork for subsequent AI research and development.

This date is significant in the history of AI because it marked the beginning of a concentrated effort to develop machines that could mimic cognitive functions such as learning, reasoning, and problem-solving.