H13-311_V3.5 Huawei HCIA-AI V3.5 Exam Free Practice Exam Questions (2025 Updated)

MindSpore provides several callback functions that can be used to monitor, modify, or control the behavior of the training process. These include:

SummaryCollector: Collects summaries such as loss and accuracy for visualization and monitoring.

ModelCheckpoint: Saves model parameters during or after training.

LossMonitor: Monitors the loss values during training and can stop training if certain conditions are met.

TrainStep is not a callback but rather a fundamental step in training.

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.

Based on the image you've described, here's how the inputs and outputs of a Generative Adversarial Network (GAN) should be matched:

Gaussian white noise vector: This is typically used as an Input to the generator in GANs. The generator uses this random noise vector to produce synthetic data.

Sample data vector: This is an Input to the discriminator. The discriminator in a GAN receives either real data from the training set or fake data generated by the generator to determine its authenticity.

Real sample data or generated sample data: This is the Output from the generator. The generator creates synthetic data that mimics the real training data, which is then fed into the discriminator.

True or false: This is the Output from the discriminator. The discriminator outputs a judgment about whether the input data it received (either from real datasets or generated by the generator) is real (true) or fake (false).

These matches align with how GANs are designed to operate, with the generator creating data and the discriminator evaluating it.

In a fully-connected layer, the number of parameters is calculated by multiplying the number of input features by the number of neurons in the hidden layer. For an image of resolution 100×100=10,000100 \times 100 = 10,000100×100=10,000 pixels and a hidden layer of 1,000 neurons, the total number of parameters is 10,000×1,000=1,000,00010,000 \times 1,000 = 1,000,00010,000×1,000=1,000,000.

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.

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.

As the model complexity increases (for example, by adding more layers to a neural network or increasing the depth of a decision tree), the training error tends to decrease. This is because more complex models are able to fit the training data better, possibly even capturing noise. However, increasing complexity often leads to overfitting, where the model performs well on the training data but poorly on unseen test data.

The relationship between model complexity and performance is covered extensively in Huawei HCIA AI's discussion of overfitting and underfitting and how model generalization is affected by increasing model complexity.

HCIA AI References:

Machine Learning Overview: Explains model complexity and its effect on training and testing error curves.

Deep Learning Overview: Discusses the balance between model capacity, overfitting, and underfitting in deep learning architectures.

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.

In MindSpore, callbacks can be used in various scenarios such as:

Early stopping: To stop training when the performance plateaus or certain criteria are met.

Saving model parameters: To save checkpoints during or after training using the ModelCheckpoint callback.

Monitoring loss values: To keep track of loss values during training using LossMonitor, allowing interventions if necessary.

Adjusting the activation function is not a typical use case for callbacks, as activation functions are usually set during model definition.

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 order of a tensor refers to its rank, which is the number of dimensions it has. For the tensor [[0,1],[2,3]], the rank is 2 because it is a 2x2 matrix, meaning it has 2 dimensions.

In machine learning, historical data is crucial for model training and prediction. The model learns from this data, identifying patterns and relationships between features and target variables. While the training algorithm is necessary for defining how the model learns, the input required for the model is historical data, as it serves as the foundation for training the model to make future predictions.

Neural networks and training algorithms are parts of the model development process, but they are not the actual input for model training.

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.

HCIA AI References:

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.

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.

Google introduced the concept of the knowledge graph in 2012, and it played a significant role in improving the search engine's ability to understand the relationships between different entities (e.g., people, places, things). This allowed Google to provide richer, more relevant search results by moving from keyword-based search to a more semantic understanding of the user's query. The knowledge graph helps organize information in a more structured way, making it easier for users to find relevant answers quickly and enhancing the overall search experience.

HCIA AI References:

AI Overview: Discusses the impact of knowledge graphs on search engines and their importance in improving AI-driven user experiences.

Cutting-edge AI Applications: Provides insights into how knowledge graphs are applied in AI systems for improving information retrieval.

In standard Recurrent Neural Networks (RNNs), the Tanh activation function is commonly used in the hidden layers. The Tanh function squashes input values to a range between -1 and 1, allowing the network to learn complex patterns over time by transforming the input data into non-linear patterns.

While other activation functions like Sigmoid can be used, Tanh is preferred in many RNNs for its wider range. ReLU is generally used in feed-forward networks, and Softmax is often applied in the output layer for classification problems.

HCIA AI References:

Deep Learning Overview: Describes the architecture of RNNs, highlighting the use of Tanh as the standard activation function.

AI Development Framework: Discusses the various activation functions used across different neural network architectures.