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

In machine learning:

The testing set is a dataset used after training to evaluate the model’s performance and generalization ability. Each sample in this set is called a test sample.

A dataset generally has multiple dimensions, with each dimension representing a feature or attribute of the data.

A typical machine learning process divides the data into a training set (to train the model), a validation set (to tune hyperparameters and avoid overfitting), and a test set (to evaluate the model’s final performance).

The statement that the validation set and test set are the same is false because they serve different purposes: validation is for hyperparameter tuning, while testing is for final model evaluation.

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.

Huawei Cloud EIHealth is a comprehensive platform that offers AI-powered solutions across various healthcare-related fields such as:

Drug R&D: Accelerates drug discovery and development using AI.

Clinical research: Enhances research efficiency through AI data analysis.

Diagnosis and treatment: Provides AI-based diagnostic support and treatment recommendations.

Genome analysis: Uses AI to analyze genetic data for medical research and personalized medicine.

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.

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.

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 nn module in MindSpore provides essential tools for building neural networks, including:

C. Optimizers: such as Momentum and Adam, which are used to adjust the weights of the model during training.

D. Loss functions: such as MSELoss (Mean Squared Error Loss) and SoftmaxCrossEntropyWithLogits, which are used to compute the difference between predicted and actual values.

The other options are incorrect because:

A. Hyperparameter search modes (like GridSearch and RandomSearch) are typically found in model training and tuning modules, but not in the nn module.

B. Model evaluation indicators like F1 Score and AUC are also handled by specific evaluation functions or libraries outside the nn module.

HCIA AI References:

AI Development Framework: Detailed coverage of MindSpore’s nn module, its optimizers, and loss functions.

Introduction to Huawei AI Platforms: Explains various MindSpore features, including network construction and training.

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.