H13-311_V3.5 Practice Exam Questions with Answers HCIA-AI V3.5 Exam Certification

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.

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:

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.

To correctly connect the layers in the architecture of an Ascend AI Processor, here’s a typical hierarchy and explanation for each layer:

Computing resources: This is generally considered as the lowest level in an AI processor architecture, directly related to the physical hardware. It should be matched with L0, which represents the hardware layer where the basic computational resources are managed.

Chip enablement: This typically involves firmware or low-level software that directly interacts with the chip hardware to enable its functionalities. This layer facilitates the operation of the hardware and is vital for the execution of any higher-level processes. It should be linked with L1.

Execution framework: This is the middle layer where higher-level frameworks that facilitate the development and execution of AI models operate. Frameworks like TensorFlow, PyTorch, or Huawei’s MindSpore would operate at this level. Connect this to L2.

Application enablement: This is the topmost layer and involves the use of AI capabilities within application-specific contexts, often utilizing the frameworks and computing resources provided by the lower layers. It should be matched with L3, indicating the highest level of abstraction in the architecture focused on applications.

This hierarchy reflects how each layer builds upon the lower ones to enable more complex functionalities and user interactions with AI technologies in processors like Huawei's Ascend.

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.

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.

When building a decision tree, the steps generally involve:

Decision tree generation: This is the process where the model iteratively splits the data based on feature values to form branches.

Pruning: This step occurs post-generation, where unnecessary branches are removed to reduce overfitting and enhance generalization.

Feature selection: This is part of decision tree construction, where relevant features are selected at each node to determine how the tree branches.

Data cleansing, on the other hand, is a preprocessing step carried out before any model training begins. It involves handling missing or erroneous data to improve the quality of the dataset but is not part of the decision tree building process itself.

HCIA AI References:

Machine Learning Overview: Includes a discussion on decision tree algorithms and the process of building decision trees.

AI Development Framework: Highlights the steps for building machine learning models, separating data preprocessing (e.g., data cleansing) from model building steps.

AI is not limited to just computer vision and speech processing. In addition to these fields, AI encompasses other important areas such as natural language processing (NLP), robotics, smart finance, autonomous driving, and more. Natural language processing focuses on understanding and generating human language, while other fields apply AI to various industries and applications such as healthcare, finance, and manufacturing. AI is a broad field with numerous application areas.

Huawei Cloud EI (Enterprise Intelligence) provides a variety of AI services and platforms, including knowledge graph, OCR (Optical Character Recognition), machine translation, and the Celia virtual assistant development platform. These services enable businesses to integrate AI capabilities such as language processing, image recognition, and virtual assistant development into their systems.

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.

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 speech recognition technology has improved significantly, its accuracy can still be affected by external factors such as noise, background sound, accents, and speech clarity. Although systems can achieve over 90% accuracy under controlled conditions, the accuracy drops in noisy or complex real-world environments. Therefore, the statement that today's speech processing technology can always achieve high recognition accuracy is incorrect.

Speech recognition systems are sophisticated but still face challenges in environments with heavy noise, where the technology has difficulty interpreting speech accurately.