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Huawei H13-311_V3.5 Exam Questions
- Huawei Certified ICT Associate HCIA Certifications
- Huawei HCIA AI Certifications
- Topic 1: AI Overview: This section of the exam focuses on the fundamental concepts of Artificial Intelligence (AI). It evaluates the target audience’s understanding of AI’s historical development, its various applications, and its impact across different industries. The target group includes data scientists and AI engineers.
- Topic 2: Machine Learning Overview: This portion tests the knowledge of machine learning engineers and covers core machine learning concepts. It focuses on methods like supervised, unsupervised, and reinforcement learning, in addition to key algorithms such as decision trees, neural networks, and regression models.
- Topic 3: Deep Learning Overview: In this part, deep learning specialists are evaluated on their expertise in deep learning theories and applications. Key topics include neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and processes like backpropagation and gradient descent.
- Topic 4: Mainstream AI Development Frameworks: This section tests AI practitioners on their proficiency with popular AI development frameworks like TensorFlow, PyTorch, and Keras.
- Topic 5: Huawei AI Development Framework MindSpore: This segment covers Huawei's proprietary AI framework, MindSpore. It evaluates AI developers' understanding of the framework’s structure and its efficiency in handling real-time AI tasks.
- Topic 6: Traditional Machine Learning Algorithms: This section covers traditional machine learning algorithms, including linear regression, decision trees, and support vector machines, which continue to play an essential role in AI.
- Topic 7: Full-Stack All-Scenario AI Strategy: This section covers full-stack AI solutions that integrate a range of AI technologies into a cohesive framework, allowing businesses to apply AI across multiple scenarios.
Free Huawei H13-311_V3.5 Exam Actual Questions
Note: Premium Questions for H13-311_V3.5 were last updated On Apr. 18, 2025 (see below)
HarmonyOS can provide AI capabilities for external systems only through the integrated HMS Core.
HarmonyOS provides AI capabilities not only through HMS Core (Huawei Mobile Services Core), but also through other system-level integrations and AI frameworks. While HMS Core is one way to offer AI functionalities, HarmonyOS also has native support for AI processing that can be accessed by external systems or applications beyond HMS Core.
Thus, the statement is false as AI capabilities are not limited solely to HMS Core in HarmonyOS.
HCIA AI
Introduction to Huawei AI Platforms: Covers HarmonyOS and the various ways it integrates AI capabilities into external systems.
Which of the following are common gradient descent methods?
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.
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 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.
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