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| Section | Objectives |
|---|---|
| Data Processing | - Feature Engineering - Data Collection and Cleaning - Data Labeling and Preparation |
| Machine Learning | - Supervised Learning
|
| Model Deployment and Operations | - Model Deployment Strategies - Inference Services - Monitoring and Maintenance |
| AI Fundamentals | - Common AI Use Cases in Industry - AI Development Lifecycle - Introduction to Artificial Intelligence |
| Model Development with Huawei ModelArts | - ModelArts Platform Overview - AutoML Capabilities - Training Models on ModelArts |
| Deep Learning | - CNN and RNN Architectures - Neural Network Fundamentals - Model Training and Optimization |
| Huawei AI Ecosystem Tools | - AI Development Toolchain - MindSpore Framework Basics - Huawei Cloud AI Services |
| AI Application Development (EI) | - Enterprise Intelligence (EI) Concepts - AI Service Integration - Building AI Applications |
1. Among image preprocessing techniques, gamma correction is a common non-linear brightness adjustment method. Which of the following statements are true about the application and features of gamma correction?
A) When # < 1, the input high grayscale range is compressed, and the low grayscale range is stretched, enhancing the dark areas while compressing the bright areas.
B) When # > 1, the input low grayscale range is compressed, and the high grayscale range is stretched, enhancing the bright areas while compressing the dark areas.
C) Gamma correction applies only to grayscale images and does not apply to color images.
D) Gamma correction is an enhancement technique based on exponential transformation mapping. It is used for non-linear contrast stretching.
2. If OpenCV is used to read an image and save it to variable "img" during image preprocessing, (h, w) = img.
shape[:2] can be used to obtain the image size.
A) TRUE
B) FALSE
3. John wants to deploy a large model locally to implement the Q&A assistant function for his company. Which of the following factors is unnecessary for John to consider?
A) Model development framework
B) Model security
C) Output delay
D) Demand for computing power
4. Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).
A) TRUE
B) FALSE
5. The deep neural network (DNN)-hidden Markov model (HMM) does not require the HMM-Gaussian mixture model (GMM) as an auxiliary.
A) TRUE
B) FALSE
Solutions:
| Question # 1 Answer: A,B,D | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: A | Question # 5 Answer: B |
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