Huawei HCIA-AI V3.5 Exam H13-311_V3.5 Exam Dumps: Updated Questions & Answers (October 2025)

Artificial intelligence is a broad field that encompasses several subfields. Two key subfields are:

Expert systems, which are computer programs that mimic the decision-making abilities of a human expert by reasoning through bodies of knowledge. These systems are used in various domains such as healthcare, engineering, and finance.

Computer vision, which enables machines to interpret and understand visual data from the world. It includes tasks such as object detection, image recognition, and video analysis.

While options like backpropagation and smart finance are related to AI, they represent specific algorithms or application areas rather than broad subfields.

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 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.

Atlas is a key part of Huawei's full-stack, all-scenario AI solution. It provides AI hardware resources in the form of modules, boards, edge stations, and servers powered by Huawei's Ascend AI processors. The Atlas series is designed to meet customer demands for AI computing power in a variety of deployment scenarios, including cloud, edge, and device environments.

Huawei’s full-stack AI solution aims to deliver comprehensive AI capabilities across different levels. The Atlas series supports a wide range of industries by offering scalable AI computing resources, which are critical for industries dealing with large volumes of data and needing high-performance computing.

The gradient descent method is a core optimization technique in machine learning, particularly for neural networks and deep learning models. The common gradient descent methods include:

Batch Gradient Descent (BGD): Updates the model parameters after computing the gradients from the entire dataset.

Mini-batch Gradient Descent (MBGD): Updates the model parameters using a small batch of data, combining the benefits of both batch and stochastic gradient descent.

Stochastic Gradient Descent (SGD): Updates the model parameters for each individual data point, leading to faster but noisier updates.

Multi-dimensional gradient descent is not a recognized method in AI or machine learning.

CANN (Compute Architecture for Neural Networks) is part of Huawei's full-stack AI solution, which includes Ascend (hardware), MindSpore (AI framework), and ModelArts (AI development platform). CANN optimizes the computing efficiency of AI models and provides basic software components for the Ascend AI processors. This architecture supports deep learning and machine learning tasks by enhancing computational performance and providing better neural network training efficiency.

Together, Ascend, MindSpore, and CANN form a critical infrastructure that underpins Huawei’s AI development ecosystem, allowing seamless integration from hardware to software.

In MindSpore, using ops.Ones() with a specified shape and dtype=mindspore.float32 will create a tensor of ones with floating-point values. The output will be a 2x2 matrix filled with 1.0 values. The floating-point format (with a decimal point) ensures that the output is in the form of [[1. 1.], [1. 1.]].

In MindSpore, when you define a tensor using Tensor(np.array([[1, 2], [3, 4]]), dtype.int32), the dtype attribute returns the data type of the tensor, which in this case is mindspore.int32. This specifies the type of elements in the tensor.

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.