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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 Jul. 05, 2025 (see below)
Huawei Cloud ModelArts provides ModelBox for device-edge-cloud joint development. Which of the following are its optimization policies?
Huawei Cloud ModelArts provides ModelBox, a tool for device-edge-cloud joint development, enabling efficient deployment across multiple environments. Some of its key optimization policies include:
Hardware affinity: Ensures that the models are optimized to run efficiently on the target hardware.
Operator optimization: Improves the performance of AI operators for better model execution.
Automatic segmentation of operators: Automatically segments operators for optimized distribution across devices, edges, and clouds.
Model replication is not an optimization policy offered by ModelBox.
AI inference chips need to be optimized and are thus more complex than those used for training.
AI inference chips are generally simpler than training chips because inference involves running a trained model on new data, which requires fewer computations compared to the training phase. Training chips need to perform more complex tasks like backpropagation, gradient calculations, and frequent parameter updates. Inference, on the other hand, mostly involves forward pass computations, making inference chips optimized for speed and efficiency but not necessarily more complex than training chips.
Thus, the statement is false because inference chips are optimized for simpler tasks compared to training chips.
HCIA AI
Cutting-edge AI Applications: Describes the difference between AI inference and training chips, focusing on their respective optimizations.
Deep Learning Overview: Explains the distinction between the processes of training and inference, and how hardware is optimized accordingly.
Huawei Cloud ModelArts provides ModelBox for device-edge-cloud joint development. Which of the following are its optimization policies?
Huawei Cloud ModelArts provides ModelBox, a tool for device-edge-cloud joint development, enabling efficient deployment across multiple environments. Some of its key optimization policies include:
Hardware affinity: Ensures that the models are optimized to run efficiently on the target hardware.
Operator optimization: Improves the performance of AI operators for better model execution.
Automatic segmentation of operators: Automatically segments operators for optimized distribution across devices, edges, and clouds.
Model replication is not an optimization policy offered by ModelBox.
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.
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