Pass the Huawei HCIP-AI EI Developer H13-321_V2.5 Questions and answers with CertsForce

TheIoUmetric is defined as:

IoU = (Area of Overlap) / (Area of Union)

Numerator:Area of overlap between the predicted bounding box and the ground truth bounding box.

Denominator:Area of union of both bounding boxes.

The statement given in the questionreversesthe numerator and denominator, which is why it is incorrect. IoU is crucial for object detection evaluation, and higher IoU values indicate better localization accuracy.

Exact Extract from HCIP-AI EI Developer V2.5:

"Intersection over Union (IoU) is calculated as the ratio of the intersection area between prediction and ground truth bounding boxes to their union area."

Word2VecandFastTextare neural network–based algorithms designed for generating dense vector representations of words.

BERTis a transformer-based language model that also generates contextualized word embeddings.

TextCNN, however, is a text classification model, not a word vector training algorithm. It uses convolutional neural networks to extract features from already vectorized text but does not learn static word embeddings in the same sense as Word2Vec or FastText.

Exact Extract from HCIP-AI EI Developer V2.5:

"Word2Vec, FastText, and BERT can be used to train word embeddings. TextCNN is a classification model that uses embeddings but does not train them as its primary function."

In anencoder-decoderarchitecture:

Theencoderprocesses variable-length inputs and encodes them into fixed-length context vectors that summarize the input. (Cis correct.)

Thedecodergenerates output sequences from this context, which may be of variable length.

Adescribes the decoder incorrectly;Bmixes roles;Dis false because output length depends on the target sequence, not the encoder output length.

Exact Extract from HCIP-AI EI Developer V2.5:

"The encoder transforms variable-length sequences into context vectors, which the decoder uses to generate variable-length outputs."

ModelArts provides an integrated environment for data labeling, model training, deployment, and management, supporting various AI application scenarios:

Image classificationfor categorizing visual content.

Object detectionfor locating and identifying multiple objects in images or video frames.

Speech recognitionfor converting speech to text.

Video analyticsfor automated video content analysis.

Exact Extract from HCIP-AI EI Developer V2.5:

"ModelArts supports a wide range of AI tasks including image classification, object detection, speech recognition, and intelligent video analytics."

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."

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."

Traditional word representations like one-hot vectors are sparse and orthogonal, failing to capture semantic similarities.Distributed semantic representations(word embeddings) map words to dense, continuous vectors in a multi-dimensional space where similar words have similar vector representations. This approach enables better generalization and semantic reasoning in NLP tasks.

Exact Extract from HCIP-AI EI Developer V2.5:

"Distributed semantic representation maps words to dense real-valued vectors in continuous space, allowing semantic similarity to be captured in vector geometry."

In CNN usage:

A:True — color images have multiple channels (e.g., RGB = 3), while text inputs are represented as sequences of word embeddings, typically single-channel in structure.

B:True — in text tasks, the convolution kernel height must match the embedding dimension to capture complete token information, which is not a constraint in images.

C:False — there are clear differences in handling between text and image data.

D:False — CNNs can perform very well in text classification when used appropriately.

Exact Extract from HCIP-AI EI Developer V2.5:

"In text CNNs, convolution kernels span the entire embedding dimension, whereas in image CNNs, kernel size is independent of channel count."