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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Model Deployment and Operations | - Monitoring and Maintenance - Inference Services - Model Deployment Strategies |
| Topic 2: AI Fundamentals | - Introduction to Artificial Intelligence - Common AI Use Cases in Industry - AI Development Lifecycle |
| Topic 3: Huawei AI Ecosystem Tools | - MindSpore Framework Basics - Huawei Cloud AI Services - AI Development Toolchain |
| Topic 4: Data Processing | - Data Collection and Cleaning - Data Labeling and Preparation - Feature Engineering |
| Topic 5: Machine Learning | - Supervised Learning
|
| Topic 6: Model Development with Huawei ModelArts | - AutoML Capabilities - Training Models on ModelArts - ModelArts Platform Overview |
| Topic 7: Deep Learning | - Neural Network Fundamentals - Model Training and Optimization - CNN and RNN Architectures |
| Topic 8: AI Application Development (EI) | - Building AI Applications - Enterprise Intelligence (EI) Concepts - AI Service Integration |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
In the image recognition algorithm, the structure design of the convolutional layer has a great impact on its performance. Which of the following statements are true about the structure and mechanism of the convolutional layer? (Transposed convolution is not considered.)
- A. The convolutional layer uses parameter sharing so that features at different positions share the same group of parameters. This reduces the number of network parameters required but reduces the expression capabilities of models.
- B. In the convolutional layer, each neuron only collects some information. This effectively reduces the memory required.
- C. The convolutional layer slides over the input feature map using a convolution kernel of a fixed size to extract local features without explicitly defining their features.
- D. A stride in the convolutional layer can control the spatial resolution of the output feature map. A larger stride indicates a smaller output feature map and simpler calculation.
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Maximum likelihood estimation (MLE) requires knowledge of the sample data's distribution type.
- A. TRUE
- B. FALSE
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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.)
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
Which of the following is not an algorithm for training word vectors?
- A. FastText
- B. Word2Vec
- C. BERT
- D. TextCNN
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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
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Camille -
The H13-321_V2.5 questions are the real ones.