Huawei H13-311_V3.5 HCIA-AI V3.5 Exam Practice Test

Answer : B

While MindSpore supports all-scenario deployment with features like data and computing graph transmission to Ascend AI processors, unified model IR for consistent deployment, and graph optimization based on software-hardware synergy, federal meta-learning is not explicitly a core feature of MindSpore's deployment strategy. Federal meta-learning refers to a distributed learning paradigm, but MindSpore focuses more on efficient computing and model optimization across different environments.

Answer : A

In MindSpore, the neural network structure is defined by inheriting the Cell class, which represents a computational node or a layer in the network. Users can customize the network by overriding the __init__ method (for initializing layers) and the construct method (for defining the forward pass of the network). This modular design allows for easy and flexible neural network construction.

Thus, the statement is true because MindSpore's framework allows developers to build neural networks by extending the Cell class and defining custom behavior through the __init__ and construct methods.

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AI Development Framework: Detailed coverage of building neural networks in MindSpore, including how to inherit from the Cell class and rewrite key methods for custom network architecture.

Answer : A

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.

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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.

Answer : B

In a convolutional neural network (CNN), each kernel (also called a filter) in the same convolutional layer does not share weights with other kernels. Each kernel is independent and learns different weights during training to detect different features in the input data. For instance, one kernel might learn to detect edges, while another might detect textures.

However, the same kernel's weights are shared across all spatial positions it moves across the input feature map. This concept of weight sharing is what makes CNNs efficient and well-suited for tasks like image recognition.

Thus, the statement that all kernels share weights is false.

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Deep Learning Overview: Detailed description of CNNs, focusing on kernel operations and weight sharing mechanisms within a single kernel, but not across different kernels.

Answer : B

The statement that mini-batch gradient descent (MBGD) takes less time than stochastic gradient descent (SGD) to complete an epoch when GPUs are used for parallel computing is incorrect. Here's why:

Stochastic Gradient Descent (SGD) updates the weights after each training sample, which can lead to faster updates but more noise in the gradient steps. It completes an epoch after processing all samples one by one.

Mini-batch Gradient Descent (MBGD) processes small batches of data at a time, updating the weights after each batch. While MBGD leverages the computational power of GPUs effectively for parallelization, the comparison made in this question is not about overall computation speed, but about completing an epoch.

MBGD does not necessarily complete an epoch faster than SGD, as MBGD processes multiple samples in each batch, meaning fewer updates per epoch compared to SGD, where weights are updated after every individual sample.

Therefore, the correct answer is B. FALSE, as MBGD does not always take less time than SGD for completing an epoch, even when GPUs are used for parallelization.

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AI Development Framework: Discussion of gradient descent algorithms and their efficiency on different hardware architectures like GPUs.

Answer : A

Stochastic Gradient Descent (SGD) presents the most chaotic landscape on the loss surface because it updates the model parameters for each individual training example, which can introduce a significant amount of noise into the optimization process. This leads to a less smooth and more chaotic path toward the global minimum compared to methods like batch gradient descent or mini-batch gradient descent, which provide more stable updates.

Answer : A, B, D

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.