At ValidExamDumps, we consistently monitor updates to the Huawei H13-311_V3.5 exam questions by Huawei. Whenever our team identifies changes in the exam questions,exam objectives, exam focus areas or in exam requirements, We immediately update our exam questions for both PDF and online practice exams. This commitment ensures our customers always have access to the most current and accurate questions. By preparing with these actual questions, our customers can successfully pass the Huawei HCIA-AI V3.5 exam on their first attempt without needing additional materials or study guides.
Other certification materials providers often include outdated or removed questions by Huawei in their Huawei H13-311_V3.5 exam. These outdated questions lead to customers failing their Huawei HCIA-AI V3.5 exam. In contrast, we ensure our questions bank includes only precise and up-to-date questions, guaranteeing their presence in your actual exam. Our main priority is your success in the Huawei H13-311_V3.5 exam, not profiting from selling obsolete exam questions in PDF or Online Practice Test.
Huawei's full-stack AI solution includes Ascend, MindSpore, and ModelArts. (Enter an acronym.)
CANN (Compute Architecture for Neural Networks) is part of Huawei's full-stack AI solution, which includes Ascend (hardware), MindSpore (AI framework), and ModelArts (AI development platform). CANN optimizes the computing efficiency of AI models and provides basic software components for the Ascend AI processors. This architecture supports deep learning and machine learning tasks by enhancing computational performance and providing better neural network training efficiency.
Together, Ascend, MindSpore, and CANN form a critical infrastructure that underpins Huawei's AI development ecosystem, allowing seamless integration from hardware to software.
Which of the following is NOT a key feature that enables all-scenario deployment and collaboration for MindSpore?
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.
Which of the following statements are true about the k-nearest neighbors (k-NN) algorithm?
The k-nearest neighbors (k-NN) algorithm is a non-parametric algorithm used for both classification and regression. In classification tasks, it typically uses majority voting to assign a label to a new instance based on the most common class among its nearest neighbors. The algorithm works by calculating the distance (often using Euclidean distance) between the query point and the points in the dataset, and then assigning the query point to the class that is most frequent among its k nearest neighbors.
For regression tasks, k-NN can predict the outcome based on the mean of the values of the k nearest neighbors, although this is less common than its classification use.
When learning the MindSpore framework, John learns how to use callbacks and wants to use it for AI model training. For which of the following scenarios can John use the callback?
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.
Which of the following functions are provided by the nn module of MindSpore?
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
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.
