Exam Name: HCIP-AI-EI Developer V2.5
Certification Provider: Huawei
Corresponding Certification: HCIP-AI EI Developer
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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| EI Model Development Fundamentals | 15% | - EI Service and Architecture - HiLens Framework and Skills - Model Development Process - Development Environment Setup |
| Deep Learning Fundamentals | 15% | - Optimization Algorithms - Training and Fine-tuning - CNN and RNN Architectures - Neural Network Basics |
| Natural Language Processing Application | 15% | - Named Entity Recognition - Language Model Fine-tuning - Text Preprocessing and Embedding - Text Classification Models |
| HiLens Platform Development | 20% | - Skill Development Framework - Multi-modal Data Processing - Edge Deployment Strategy - Real-time Inference Optimization |
| Image Recognition Application Development | 15% | - Image Classification Models - Transfer Learning with Pre-trained Models - Image Segmentation - Object Detection Implementation |
| ModelArts Pro Development | 20% | - AutoML and Automatic Model Training - Model Deployment and Management - Inference Service Configuration - Hyperparameter Optimization |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
Which of the following statements about the functions of layer normalization and residual connection in the Transformer is true?
- A. Layer normalization accelerates model convergence and does not affect model stability.
- B. In shallow networks, residual connections are beneficial, but they aggravate the vanishing gradient problem in deep networks.
- C. Residual connections and layer normalization help prevent vanishing gradients and exploding gradients in deep networks.
- D. Residual connections primarily add depth to the model but do not aid in gradient propagation.
Correct Answer: C 🗳️
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
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. Demand for computing power
- C. Model security
- D. Output delay
Correct Answer: A 🗳️
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).
- A. FALSE
- B. TRUE
Correct Answer: B 🗳️
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
In natural language processing tasks, word vector evaluation is an important aspect for measuring the performance of a word embedding model. Which of the following statements about word vector evaluation are true?
- A. Word similarity tasks typically employ manually labeled datasets to evaluate word vectors, compute the cosine similarity between word vectors, and compare it with the manual labeling result.
- B. The word analogy task evaluates the capability of word vectors in capturing semantic relationships between words, for example, by determining whether "king - man + woman = ?" is close to "queen".
- C. Extrinsic evaluation is the main method used for evaluating word vectors because it directly reflects the performance of word vectors in real-world application tasks.
- D. Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
Correct Answer: A,B,D 🗳️
Explanation: Only visible for Fast2test members. You can sign-up / login (it's free).
In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. The Transformer consists of an encoder and a(n) --------. (Fill in the blank.)
Reveal Solution Discussion 0Correct Answer:
Decoder
Explanation:
The Transformer model architecture includes:
* Encoder:Encodes the input sequence into contextualized representations.
* Decoder:Uses the encoder output and self-attention over previously generated tokens to produce the target sequence.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Transformer consists of an encoder-decoder structure, with self-attention mechanisms in both components for sequence-to-sequence learning." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Overview
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