Free Huawei H13-311_V3.5 Practice Exam with Questions & Answers

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.

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.

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.

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.

A. TRUE. The common decision tree algorithms include ID3, C4.5, and CART. These are the most widely used algorithms for decision tree generation.B. FALSE. Purity in decision trees can be measured using multiple metrics, such as information gain, Gini index, and others, not just information entropy.C. TRUE. Building a decision tree involves selecting the best features and determining their order in the tree structure to split the data effectively.D. TRUE. One key step in decision tree generation is evaluating the purity of different splits (e.g., how well the split segregates the target variable) by comparing metrics like information gain or Gini index.

HCIA AI References:

Machine Learning Overview: Covers decision tree algorithms and their use cases.

Deep Learning Overview: While this focuses on neural networks, it touches on how decision-making algorithms are used in structured data models.

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.

This statement is false because not all feedforward neural networks follow this architecture. While fully-connected layers do have this type of connectivity (where each neuron is connected to all neurons in the previous layer), feedforward networks can include layers like convolutional layers, where not every neuron is connected to all previous neurons. Convolutional layers, common in convolutional neural networks (CNNs), only connect to a local region of the input, preserving spatial information.

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.

The process of constructing a knowledge graph typically involves several key steps:

A. Determining the target domain of the knowledge graph: This defines the scope and boundaries of the information to be represented.

B. Data acquisition: Involves gathering structured and unstructured data from various sources.

D. Knowledge fusion: This step involves integrating and reconciling data from multiple sources to create a consistent and coherent knowledge graph.

Creating new concepts is not typically part of the knowledge graph construction process. Instead, knowledge graphs usually focus on extracting, integrating, and structuring existing knowledge, not creating new concepts.

HCIA AI References:

AI Development Framework: Describes the steps in constructing knowledge graphs, from data acquisition to knowledge fusion and domain determination.