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Huawei H13-311_V3.5 Exam Syllabus Topics:

Topic	Details
Topic 1	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 2	Mainstream AI Development Frameworks: This section tests AI practitioners on their proficiency with popular AI development frameworks like TensorFlow, PyTorch, and Keras.
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	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 5	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 6	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.