H13-321_V2.5 Certification Exam Guide + Practice Questions Updated 2026

Explanation:
The Vision Transformer (ViT) applies the transformer architecture to image patches. Its key advantage is the use of self-attention to capture global dependencies and relationships between all parts of an image. This allows ViT to excel in classification accuracy, especially on large datasets with sufficient pre-training.
Exact Extract from HCIP-AI EI Developer V2.5:
"ViT applies self-attention to image patches, enabling global feature extraction and improving classification performance compared to local receptive fields in CNNs."
Reference: HCIP-AI EI Developer V2.5 Official Study Guide C Chapter: Transformer Models in Vision

Explanation:
Huawei Cloud’s TTS service supports generating synthesized speech in multiple formats to meet different application requirements:
WAV: High-quality, uncompressed audio format.
MP3: Compressed format for efficient storage and streaming.
PCM: Raw, uncompressed audio data for low-level processing.
AAC is a common format but is not supported by Huawei Cloud TTS as per the official specification for HCIP-AI EI Developer V2.5.
Exact Extract from HCIP-AI EI Developer V2.5:
"Huawei Cloud TTS supports output in WAV, MP3, and PCM formats to balance audio quality and storage requirements."
Reference: HCIP-AI EI Developer V2.5 Official Study Guide C Chapter: Text-to-Speech Service

Explanation:
Deep learningCbased speech recognition offers two key advantages over traditional approaches:
Automated feature extraction (B): Neural networks can directly learn features from raw or lightly processed audio without manual engineering of MFCCs or filter banks.
End-to-end task processing (C): Models like CTC-based networks or attention-based architectures can map audio inputs directly to text outputs without intermediate models like GMM-HMM.
Options A and D are incorrect because forced alignment is part of traditional GMM-HMM systems, and deep learning still requires training with large datasets.
Exact Extract from HCIP-AI EI Developer V2.5:
"Deep learning models support automatic feature extraction and can implement end-to-end mapping from speech signals to text outputs."
Reference: HCIP-AI EI Developer V2.5 Official Study Guide C Chapter: End-to-End Speech Recognition

Explanation:
NER (Named Entity Recognition) is a core NLP task that involves locating and categorizing entities within text into predefined categories like persons, organizations, places, dates, monetary values, and percentages. NER is widely used in information extraction, question answering, and knowledge graph construction.
Exact Extract from HCIP-AI EI Developer V2.5:
"NER identifies and classifies named entities in text into categories such as person names, organizations, locations, time expressions, and numeric values."
Reference: HCIP-AI EI Developer V2.5 Official Study Guide C Chapter: Sequence Labeling Tasks

Explanation:
In OpenCV:
● cv2.imread (filename, 1) reads the image incolor mode.
● This loads the image as a3-channelBGR image (Blue, Green, Red).
● Other modes: 0 for grayscale, -1 for unchanged (including alpha channel).
Exact Extract from HCIP-AI EI Developer V2.5:
"When the second parameter of cv2.imread is 1, the image is read in color mode, resulting in a 3-channel BGR image."
Reference: HCIP-AI EI Developer V2.5 Official Study Guide C Chapter: Image Reading and Writing with OpenCV