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Huawei H13-321_V2.5 Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Model Development with Huawei ModelArts | - ModelArts Platform Overview - Training Models on ModelArts - AutoML Capabilities |
| Deep Learning | - CNN and RNN Architectures - Neural Network Fundamentals - Model Training and Optimization |
| Data Processing | - Data Collection and Cleaning - Data Labeling and Preparation - Feature Engineering |
| AI Fundamentals | - Introduction to Artificial Intelligence - AI Development Lifecycle - Common AI Use Cases in Industry |
| Huawei AI Ecosystem Tools | - Huawei Cloud AI Services - MindSpore Framework Basics - AI Development Toolchain |
| Model Deployment and Operations | - Monitoring and Maintenance - Inference Services - Model Deployment Strategies |
| Machine Learning | - Supervised Learning
|
| AI Application Development (EI) | - Building AI Applications - Enterprise Intelligence (EI) Concepts - AI Service Integration |
Huawei HCIP-AI-EI Developer V2.5 Sample Questions:
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
-------- is a text representation method based on the bag of words (BoW) model. It decomposes words into subwords and then adds the vector representations of the subwords to obtain word vectors, fully utilizing character N-gram information. (Fill in the blank.)
Reveal Solution Discussion 0Correct Answer:
FastText
Explanation:
FastTextis an extension of Word2Vec developed by Facebook AI Research. Unlike Word2Vec, which learns embeddings for whole words, FastText represents each word as a sum of its character n-gram embeddings.
This helps in handling rare words and morphologically rich languages by generating embeddings for unseen words from their subword components.
Exact Extract from HCIP-AI EI Developer V2.5:
"FastText decomposes words into character n-grams and represents words as the sum of their n-gram vectors, improving representation for rare and out-of-vocabulary words." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Subword Embedding Models
The accuracy of object location detection can be evaluated using the intersection over union (IoU) value, which is a ratio. The denominator is the overlapping area between the prediction bounding box and ground truth bounding box, and the numerator is the area of union encompassed by both boxes.
- A. FALSE
- B. TRUE
Correct Answer: A 🗳️
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The U-Net uses an upsampling mechanism and has a fully-connected layer.
- A. FALSE
- B. TRUE
Correct Answer: A 🗳️
Explanation: Only visible for BraindumpsIT members. You can sign-up / login (it's free).
Which of the following applications are supported by ModelArts ExeML?
- A. Automatic offering classification
- B. Anomalous sound detection in production or security scenarios
- C. Dress code conformance monitoring in campuses
- D. Predictive maintenance of manufacturing equipment
Correct Answer: A,B,C,D 🗳️
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