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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: ModelArts Pro Development | 20% | - Hyperparameter Optimization - Inference Service Configuration - AutoML and Automatic Model Training - Model Deployment and Management |
| Topic 2: Deep Learning Fundamentals | 15% | - Training and Fine-tuning - Neural Network Basics - CNN and RNN Architectures - Optimization Algorithms |
| Topic 3: HiLens Platform Development | 20% | - Real-time Inference Optimization - Skill Development Framework - Edge Deployment Strategy - Multi-modal Data Processing |
| Topic 4: Natural Language Processing Application | 15% | - Named Entity Recognition - Language Model Fine-tuning - Text Classification Models - Text Preprocessing and Embedding |
| Topic 5: Image Recognition Application Development | 15% | - Image Classification Models - Object Detection Implementation - Image Segmentation - Transfer Learning with Pre-trained Models |
| Topic 6: EI Model Development Fundamentals | 15% | - HiLens Framework and Skills - Model Development Process - Development Environment Setup - EI Service and Architecture |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
Seq2Seq is a model that translates one sequence into another sequence, essentially consisting of two recurrent neural networks (RNNs), one is the Encoder, and the other is the ---------. (Fill in the blank.)
Reveal Solution Discussion 0Correct Answer:
Decoder
Explanation:
The Seq2Seq architecture is widely used in machine translation, speech recognition, and other NLP tasks. It consists of:
* Encoder:Processes the input sequence and encodes it into a fixed-length context vector containing semantic information.
* Decoder:Uses this context vector to generate the target output sequence step by step.
Exact Extract from HCIP-AI EI Developer V2.5:
"Seq2Seq models consist of an encoder and a decoder. The encoder transforms the input into a context vector, which the decoder uses to generate the output sequence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Encoder-Decoder Architecture
Maximum likelihood estimation (MLE) requires knowledge of the sample data's distribution type.
- A. FALSE
- B. TRUE
Correct Answer: B 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
Which of the following statements about the standard normal distribution are true?
- A. The variance is 1.
- B. The mean is 1.
- C. The variance is 0.
- D. The mean is 0.
Correct Answer: A,D 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
When training a deep neural network model, a loss function measures the difference between the model's predictions and the actual labels.
- A. FALSE
- B. TRUE
Correct Answer: B 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
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. Gamma correction is an enhancement technique based on exponential transformation mapping. It is used for non-linear contrast stretching.
- 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. 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.
Correct Answer: A,B,D 🗳️
Explanation: Only visible for Lead2Passed members. You can sign-up / login (it's free).
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