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| Section | Weight | Objectives |
|---|---|---|
| Natural Language Processing Application | 15% | - Text Classification Models - Text Preprocessing and Embedding - Language Model Fine-tuning - Named Entity Recognition |
| ModelArts Pro Development | 20% | - Model Deployment and Management - AutoML and Automatic Model Training - Hyperparameter Optimization - Inference Service Configuration |
| Image Recognition Application Development | 15% | - Transfer Learning with Pre-trained Models - Image Classification Models - Image Segmentation - Object Detection Implementation |
| Deep Learning Fundamentals | 15% | - Optimization Algorithms - Training and Fine-tuning - CNN and RNN Architectures - Neural Network Basics |
| HiLens Platform Development | 20% | - Edge Deployment Strategy - Real-time Inference Optimization - Skill Development Framework - Multi-modal Data Processing |
| EI Model Development Fundamentals | 15% | - HiLens Framework and Skills - EI Service and Architecture - Model Development Process - Development Environment Setup |
Question 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
Question 2
Which of the following are object detection algorithms?
A. Faster-R-CNN
B. YOLO
C. R-CNN
D. SSD
Question 3
In an image preprocessing experiment, the cv2.imread("lena.png", 1) function provided by OpenCV is used to read images. The parameter "1" in this function represents a --------- -channel image. (Fill in the blank with a number.)
Question 4
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. Normalize the attention scores to obtain attention weights.
B. Calculate the dot product similarity between the query and key vectors to obtain attention scores.
C. Apply a non-linear mapping to the result obtained after the weighted summation.
D. Compute the weighted sum of the value vectors using the attention weights.
Question 5
How many parameters need to be learned when a 3 × 3 convolution kernel is used to perform the convolution operation on two three-channel color images?
A. 10
B. 55
C. 9
D. 28
Solutions:
| Question 1 Answer: A | Question 2 Answer: A,B,C,D | Question 3 Answer: Only visible for members | Question 4 Answer: A,B,D | Question 5 Answer: D |
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