HCIP-AI EI Developer study guide
HCIP-AI EI Developer · Work through your exam, one topic at a time.
The HCIP-AI EI Developer is a Professional-level credential in Huawei's career certification roadmap, which Huawei describes as running through three tiers: Huawei Certified ICT Associate (HCIA), Huawei Certified ICT Professional (HCIP), and Huawei Certified ICT Expert (HCIE). This page is an independent study guide for candidates preparing for that exam; it is not affiliated with, endorsed by, or operated by Huawei, and it does not reproduce any official exam content. The HCIP-AI EI Developer study guide has 5 sections and about 1,391 words.
01AI Development Fundamentals and the Huawei AI Platform
Questions test whether you can place each Huawei AI component in the right stack layer and choose the correct service for data, training, or inference. Huawei describes its AI stack as full-stack, all-scenario: Ascend 310 (inference) and Ascend 910 (training) chips; CANN, the compute architecture that exposes operators to frameworks; MindSpore, the open-source framework; and ModelArts, the cloud development platform.
290 words, about 1 min02Python and AI Development Frameworks
The exam tests practical Python plus the tensor, dataset, and model APIs used in MindSpore and comparable frameworks. NumPy broadcasting aligns shapes from the right; a dimension of size 1 is stretched, otherwise the shapes must match.
271 words, about 1 min03Machine Learning and Deep Learning Model Development
This domain covers the ML/DL workflow: problem framing, data splitting, model selection, training, evaluation, and tuning, with ModelArts training jobs as the execution vehicle. Split data into train, validation, and test.
257 words, about 1 min04Computer Vision Application Development
CV questions cover classification, detection, and segmentation pipelines, data augmentation, transfer learning, and deployment on ModelArts, Atlas, and HiLens. Classification predicts one label per image; detection predicts boxes plus classes (two-stage Faster R-CNN; one-stage YOLO/SSD); semantic segmentation labels every pixel, instance segmentation separates objects.
286 words, about 1 min05Natural Language Processing Application Development
NLP questions test text preprocessing, representation learning, sequence models, Transformer/BERT fine-tuning, and Huawei NLP deployment options. Preprocessing: normalization, tokenization, vocabulary construction, padding, and truncation to a maximum length.
287 words, about 1 min