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Huawei H13-321_V2.5 Exam Overview:
| Certification Vendor: | Huawei |
| Exam Name: | HCIP-AI-EI Developer V2.5 |
| Exam Number: | H13-321_V2.5 |
| Real Exam Qty: | 60-70 |
| Exam Duration: | 90 minutes |
| Related Certifications: | HCIA-AI-EI Developer HCIE-AI-EI Developer |
| Certificate Validity Period: | 3 years |
| Available Languages: | English, Chinese |
| Exam Price: | USD 300 |
| Exam Format: | Multiple Choice, True or False, Drag and Drop, Simulation |
| Passing Score: | 600/1000 |
| Sample Questions: | Huawei H13-321_V2.5 Sample Questions |
| Exam Way: | Online/Offline Testing Center |
| Pre Condition: | Recommended: HCIA-AI-EI Developer certification or equivalent knowledge of deep learning and Huawei cloud services |
| Official Syllabus URL: | https://support.huawei.com/hedex/hdx.do?docid=DOC1000203314 |
Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| HiLens Platform Development | 20% | - Multi-modal Data Processing - Edge Deployment Strategy - Real-time Inference Optimization - Skill Development Framework |
| Natural Language Processing Application | 15% | - Named Entity Recognition - Text Preprocessing and Embedding - Language Model Fine-tuning - Text Classification Models |
| Image Recognition Application Development | 15% | - Image Classification Models - Object Detection Implementation - Transfer Learning with Pre-trained Models - Image Segmentation |
| EI Model Development Fundamentals | 15% | - EI Service and Architecture - Model Development Process - Development Environment Setup - HiLens Framework and Skills |
| Deep Learning Fundamentals | 15% | - Neural Network Basics - Optimization Algorithms - CNN and RNN Architectures - Training and Fine-tuning |
| ModelArts Pro Development | 20% | - Hyperparameter Optimization - Model Deployment and Management - Inference Service Configuration - AutoML and Automatic Model Training |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
1. Huawei Cloud ModelArts is a one-stop AI development platform that supports multiple AI scenarios. Which of the following scenarios are supported by ModelArts?
A) Image classification
B) Video analytics
C) Object detection
D) Speech recognition
2. 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) 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.
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) 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.
D) Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
3. What type of task is viewed when using the Seq2Seq model in speech recognition?
A) Regression task
B) Classification task
C) Clustering task
D) Dimensionality reduction task
4. ------- is a model that uses a convolutional neural network (CNN) to classify texts.
5. 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
Solutions:
| Question # 1 Answer: A,B,C,D | Question # 2 Answer: B,C,D | Question # 3 Answer: B | Question # 4 Answer: Only visible for members | Question # 5 Answer: A |
By Geraldine
