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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: AI Fundamentals | - AI Development Lifecycle - Common AI Use Cases in Industry - Introduction to Artificial Intelligence |
| Topic 2: Model Deployment and Operations | - Model Deployment Strategies - Monitoring and Maintenance - Inference Services |
| Topic 3: Model Development with Huawei ModelArts | - ModelArts Platform Overview - Training Models on ModelArts - AutoML Capabilities |
| Topic 4: Deep Learning | - CNN and RNN Architectures - Neural Network Fundamentals - Model Training and Optimization |
| Topic 5: Data Processing | - Data Labeling and Preparation - Data Collection and Cleaning - Feature Engineering |
| Topic 6: AI Application Development (EI) | - Enterprise Intelligence (EI) Concepts - AI Service Integration - Building AI Applications |
| Topic 7: Huawei AI Ecosystem Tools | - Huawei Cloud AI Services - MindSpore Framework Basics - AI Development Toolchain |
| Topic 8: Machine Learning | - Unsupervised Learning
|
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
Question #1
In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. In a Transformer model, there is customized LSTM with CNN layers.
A. FALSE
B. TRUE
Question #2
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).
A. FALSE
B. TRUE
Question #3
The U-Net uses an upsampling mechanism and has a fully-connected layer.
A. FALSE
B. TRUE
Question #4
Transformer models outperform LSTM when analyzing and processing long-distance dependencies, making them more effective for sequence data processing.
A. FALSE
B. TRUE
Question #5
The attention mechanism in foundation model architectures allows the model to focus on specific parts of the input data. Which of the following steps are key components of a standard attention mechanism?
A. Apply a non-linear mapping to the result obtained after the weighted summation.
B. Normalize the attention scores to obtain attention weights.
C. Calculate the dot product similarity between the query and key vectors to obtain attention scores.
D. Compute the weighted sum of the value vectors using the attention weights.
Solutions:
| Question #1 Correct Answer: A | Question #2 Correct Answer: B | Question #3 Correct Answer: A | Question #4 Correct Answer: B | Question #5 Correct Answer: B,C,D |
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