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Huawei H13-321_V2.5 Exam Overview:
| Certification Vendor: | Huawei |
| Exam Name: | Huawei HCIP-AI-EI Developer V2.5 Certification Exam |
| Exam Number: | H13-321_V2.5 |
| Passing Score: | 600/1000 |
| Available Languages: | English, Simplified Chinese |
| Exam Duration: | 90 minutes |
| Exam Format: | Single choice, Multiple choice, True/False, Drag and drop |
| Certificate Validity Period: | 3 years |
| Exam Price: | USD 200 (varies by region) |
| Real Exam Qty: | 60-70 |
| Related Certifications: | HCIA-AI HCIP-AI-EI Developer |
| Recommended Training: | Huawei HCIP-AI-EI Developer Training Huawei Cloud Academy AI Courses |
| Exam Registration: | Huawei Certification Portal Huawei Talent |
| Sample Questions: | Huawei H13-321_V2.5 Sample Questions |
| Exam Way: | Online proctored exam or authorized test center |
| Pre Condition: | Basic knowledge of AI, machine learning, and programming (Python recommended). HCIA-AI or equivalent knowledge is recommended. |
| Official Syllabus URL: | https://certification.huawei.com |
Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Topic 1: Huawei AI Ecosystem Tools | - MindSpore Framework Basics - Huawei Cloud AI Services - AI Development Toolchain |
| Topic 2: Machine Learning | - Unsupervised Learning
|
| Topic 3: Data Processing | - Data Labeling and Preparation - Feature Engineering - Data Collection and Cleaning |
| Topic 4: Deep Learning | - Model Training and Optimization - Neural Network Fundamentals - CNN and RNN Architectures |
| Topic 5: AI Application Development (EI) | - Building AI Applications - AI Service Integration - Enterprise Intelligence (EI) Concepts |
| Topic 6: Model Deployment and Operations | - Inference Services - Model Deployment Strategies - Monitoring and Maintenance |
| Topic 7: AI Fundamentals | - AI Development Lifecycle - Introduction to Artificial Intelligence - Common AI Use Cases in Industry |
| Topic 8: Model Development with Huawei ModelArts | - ModelArts Platform Overview - AutoML Capabilities - Training Models on ModelArts |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
1. Which of the following are the impacts of the development of large models?
A) Large models will completely replace small and domain-specific models
B) Data privacy and security issues will be exacerbated
C) The accuracy and efficiency of natural language processing tasks will improve
D) Model pre-training costs will be reduced
2. 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 applies only to grayscale images and does not apply to color images.
B) Gamma correction is an enhancement technique based on exponential transformation mapping. It is used for non-linear contrast stretching.
C) 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.
D) 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.
3. The mAP evaluation metric in object detection combines accuracy and recall.
A) TRUE
B) FALSE
4. 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.)
5. 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.
Solutions:
| Question # 1 Answer: B,C | Question # 2 Answer: B,C,D | Question # 3 Answer: B | Question # 4 Answer: Only visible for members | Question # 5 Answer: B,C,D |
By Erin
