Huawei H13-311_V3.5 Exam Dumps

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

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.

A . TRUE. The common decision tree algorithms include ID3, C4.5, and CART. These are the most widely used algorithms for decision tree generation.

B . FALSE. Purity in decision trees can be measured using multiple metrics, such as information gain, Gini index, and others, not just information entropy.

C . TRUE. Building a decision tree involves selecting the best features and determining their order in the tree structure to split the data effectively.

D . TRUE. One key step in decision tree generation is evaluating the purity of different splits (e.g., how well the split segregates the target variable) by comparing metrics like information gain or Gini index.

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Machine Learning Overview: Covers decision tree algorithms and their use cases.

Deep Learning Overview: While this focuses on neural networks, it touches on how decision-making algorithms are used in structured data models.

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.

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.