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Last Updated: Aug 11, 2026
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| Section | Objectives |
|---|---|
| Topic 1: Machine Learning | - Supervised Learning
|
| Topic 2: Deep Learning | - CNN and RNN Architectures - Neural Network Fundamentals - Model Training and Optimization |
| Topic 3: Model Deployment and Operations | - Inference Services - Model Deployment Strategies - Monitoring and Maintenance |
| Topic 4: Huawei AI Ecosystem Tools | - AI Development Toolchain - Huawei Cloud AI Services - MindSpore Framework Basics |
| Topic 5: Model Development with Huawei ModelArts | - AutoML Capabilities - ModelArts Platform Overview - Training Models on ModelArts |
| Topic 6: Data Processing | - Data Labeling and Preparation - Feature Engineering - Data Collection and Cleaning |
| Topic 7: AI Application Development (EI) | - Enterprise Intelligence (EI) Concepts - AI Service Integration - Building AI Applications |
| Topic 8: AI Fundamentals | - Introduction to Artificial Intelligence - AI Development Lifecycle - Common AI Use Cases in Industry |
1. The technologies underlying ModelArts support a wide range of heterogeneous compute resources, allowing you to flexibly use the resources that fit your needs.
A) TRUE
B) FALSE
2. Mel-frequency cepstral coefficients (MFCCs) take into account human auditory characteristics by first mapping the linear spectrum to the Mel nonlinear spectrum based on auditory perception, and then converting it to the cepstral domain.
A) TRUE
B) FALSE
3. 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) TRUE
B) FALSE
4. Which of the following statements about the multi-head attention mechanism of the Transformer are true?
A) The concatenated output is fed directly into the multi-headed attention mechanism.
B) The multi-head attention mechanism captures information about different subspaces within a sequence.
C) Each header's query, key, and value undergo a shared linear transformation to obtain them.
D) The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
5. The objective of -------- is to extract and classify named entities in a text into pre-defined classes such as names, organizations, locations, time expressions, monetary values, and percentages. (Enter the abbreviation.)
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: B,D | Question # 5 Answer: Only visible for members |
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