Free Practice Questions for Huawei H13-311_V3.5 Exam

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.

Atlas is a key part of Huawei's full-stack, all-scenario AI solution. It provides AI hardware resources in the form of modules, boards, edge stations, and servers powered by Huawei's Ascend AI processors. The Atlas series is designed to meet customer demands for AI computing power in a variety of deployment scenarios, including cloud, edge, and device environments.

Huawei’s full-stack AI solution aims to deliver comprehensive AI capabilities across different levels. The Atlas series supports a wide range of industries by offering scalable AI computing resources, which are critical for industries dealing with large volumes of data and needing high-performance computing.

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.

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.

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.

Huawei HMS Core (Huawei Mobile Services Core) provides a variety of AI capabilities, including:

HiAI Foundation: Offers basic AI infrastructure, enabling AI computing capabilities.

HiAI Engine: Provides pre-built AI engines for tasks like image processing and NLP.

ML Kit: Provides machine learning features for developers to integrate into apps.

MindSpore Lite is not part of HMS Core but rather a lightweight version of the MindSpore framework designed for mobile and edge devices.

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.

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.

Nesterov Accelerated Gradient (NAG) is indeed a variant of the momentum optimizer. In the traditional momentum method, the gradient is used to adjust the direction based on the current momentum. Nesterov, on the other hand, anticipates the change in the momentum by calculating the gradient at a slightly altered position. This small adjustment leads to better convergence and more efficient optimization, especially in non-convex problems.

Momentum methods and their variants like Nesterov are commonly discussed in the optimization strategies for neural networks, including frameworks such as TensorFlow, which is covered in Huawei's HCIA AI courses.

HCIA AI References:

Deep Learning Overview: Discussion of optimization algorithms, including gradient descent variants like Momentum and Nesterov.

AI Development Framework: Explains the use of Nesterov in deep learning frameworks such as TensorFlow and PyTorch.