Get H13-311_V3.5 Dumps : Verified HCIA-AI V3.5 Exam

The k-nearest neighbors (k-NN) algorithm is a non-parametric algorithm used for both classification and regression. In classification tasks, it typically uses majority voting to assign a label to a new instance based on the most common class among its nearest neighbors. The algorithm works by calculating the distance (often using Euclidean distance) between the query point and the points in the dataset, and then assigning the query point to the class that is most frequent among its k nearest neighbors.

For regression tasks, k-NN can predict the outcome based on the mean of the values of the k nearest neighbors, although this is less common than its classification use.

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.

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.

Feedforward neural networks (FNNs) are networks where information moves in only one direction—forward—from the input nodes through hidden layers to the output nodes. Both fully-connected neural networks (where each neuron in one layer connects to every neuron in the next) and convolutional neural networks (CNNs) (which have a specific architecture for image data) are examples of feedforward networks.

However, recurrent neural networks (RNNs) and Boltzmann machines are not feedforward networks. RNNs include loops where information can be fed back into previous layers, and Boltzmann machines involve undirected connections between units, making them a form of a stochastic network rather than a feedforward structure.

In MindSpore, nn.Cell is the basic unit of a neural network. It represents layers, models, and other neural network components, encapsulating the forward logic of the network. It allows users to define, organize, and manage neural network layers in MindSpore, making it a core building block in neural network construction.