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Huawei H13-311_V3.5 Exam
HCIA-AI V3.5 (Page 6 )

Updated On: 1-Feb-2026
Page 2 of 13
PDF with 60 Questions

As we understand more about machine learning, we will find that its scope is constantly changing over time.

  1. TRUE
  2. FALSE

Answer(s): A

Explanation:

Machine learning is a rapidly evolving field, and its scope indeed changes over time. With advancements in computational power, the introduction of new algorithms, frameworks, and techniques, and the growing availability of data, the capabilities of machine learning have expanded significantly. Initially, machine learning was limited to simpler algorithms like linear regression, decision trees, and k-nearest neighbors. Over time, however, more complex approaches such as deep learning and reinforcement learning have emerged, dramatically increasing the applications and effectiveness of machine learning solutions.
In the Huawei HCIA-AI curriculum, it is emphasized that AI, especially machine learning, has become more powerful due to these continuous developments, allowing it to be applied to broader and more complex problems. The framework and methodologies in machine learning have evolved, making it possible to perform more sophisticated tasks such as real-time decision-making, image recognition, natural language processing, and even autonomous driving. As technology advances, the scope of machine learning will continue to shift, providing new opportunities for innovation. This is why it is important to stay updated on recent developments to fully leverage machine learning in various AI applications.


Reference:

Huawei HCIA-AI Certification, Machine Learning Overview.



The mean squared error (MSE) loss function cannot be used for classification problems.

  1. TRUE
  2. FALSE

Answer(s): A

Explanation:

The mean squared error (MSE) loss function is primarily used for regression problems, where the goal is to minimize the difference between the predicted and actual continuous values. For classification problems, where the target output is categorical (e.g., binary or multi-class labels), loss functions like cross-entropy are more suitable, as they are designed to handle the probabilistic interpretation of outputs in classification tasks.
Using MSE for classification could lead to inefficient training because it doesn't capture the probabilistic relationships required for classification tasks.


Reference:

Huawei HCIA-AI Certification, Machine Learning ­ Loss Functions.



Which of the following statements is false about gradient descent algorithms?

  1. Each time the global gradient updates its weight, all training samples need to be calculated.
  2. When GPUs are used for parallel computing, the mini-batch gradient descent (MBGD) takes less time than the stochastic gradient descent (SGD) to complete an epoch.
  3. The global gradient descent is relatively stable, which helps the model converge to the global extremum.
  4. When there are too many samples and GPUs are not used for parallel computing, the convergence process of the global gradient algorithm is time-consuming.

Answer(s): B

Explanation:

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.


Reference:

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



All kernels of the same convolutional layer in a convolutional neural network share a weight.

  1. TRUE
  2. FALSE

Answer(s): B

Explanation:

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.


Reference:

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



The core of the MindSpore training data processing engine is to efficiently and flexibly convert training samples (datasets) to MindRecord and provide them to the training network for training.

  1. TRUE
  2. FALSE

Answer(s): A

Explanation:

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.


Reference:

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.



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