Huawei H13-321_V2.5 HCIP - AI EI Developer V2.5 Exam Exam Practice Test

When the contrast between bright and dark areas is extreme,gamma correctionis effective in adjusting luminance in a non-linear way to balance these extremes.

Ifγ < 1, dark areas are brightened, highlights are compressed.

Ifγ > 1, bright areas are emphasized, shadows are compressed.Other methods like grayscale stretching and compression target linear contrast changes, while inversion flips pixel values but doesn’t balance extreme light/dark ranges effectively.

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

"Gamma correction adjusts image brightness non-linearly, suitable for correcting overly bright or overly dark regions, improving overall visibility."

In the HCIP-AI EI Developer V2.5 study guide, the loss function is defined as a core component in training deep neural network models. It serves as a quantitative measure of how well the model's predictions match the actual ground truth labels. By calculating the difference between predicted outputs and actual labels, the loss function provides feedback that the optimization algorithm (such as gradient descent) uses to update model parameters. This process is iterative, aiming to minimize the loss value, thereby improving prediction accuracy. For example, in classification tasks,Cross-Entropy Lossis commonly used, while in regression tasks,Mean Squared Error (MSE)is typical. The smaller the loss, the better the model's performance on the given data.

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

"A loss function is an objective function that evaluates the difference between the model output and the real label. The goal of training is to minimize this loss so that the model predictions approach the actual values."

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 NLP:

Text classification(e.g., sentiment analysis) predicts a single label for the entire input sequence.

Sequence labeling(e.g., Named Entity Recognition, Part-of-Speech tagging) produces an output label for each token or position in the input sequence.This distinction is important for selecting appropriate model architectures and loss functions.

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

"Text classification assigns one label to the whole text, whereas sequence labeling assigns a label to each token in the sequence."

In speech signal processing,acoustic featuresdescribe measurable physical properties of sound waves, such asduration(time length),frequency(pitch), andamplitude(loudness). These features are used in speech recognition and speaker identification systems.

Semantics, on the other hand, refers to the meaning of speech — a linguistic attribute, not an acoustic property. Therefore, it is not classified as an acoustic feature.

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

"Speech features include duration, frequency, and amplitude. These are acoustic characteristics, distinct from semantic information, which relates to language meaning."

The emergence of large AI models (e.g., GPT, Pangu, BERT) has led to:

C:Improved accuracy and efficiency in NLP and other AI tasks because of their ability to capture deep semantic and contextual information.

D:Increased data privacy and security concerns, as large models require massive datasets which may contain sensitive or proprietary information.Ais false — large models increase pre-training costs.Bis false — small and domain-specific models still play important roles due to efficiency and deployment constraints.

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

"Large models improve task performance but raise privacy and security concerns. They do not necessarily reduce training cost or eliminate the need for smaller models."

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."

A:Incorrect — description given matches semantic analysis, not syntactic analysis.

B:Incorrect — description given matches syntactic analysis, not semantic analysis.

C:Correct — speech analysis focuses on phoneme recognition and word identification.

D:Correct — lexical analysis identifies lexemes and retrieves their linguistic details.

E:Correct — pragmatic analysis studies language use in context and environment.

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

"Natural language understanding involves lexical, syntactic, semantic, speech, and pragmatic analyses, each focusing on different layers of language processing."

ThemAP(mean Average Precision) metric in object detection combinesprecisionandrecall, not accuracy and recall. mAP is calculated by averaging the Average Precision (AP) across all classes in a dataset. Precision measures how many predicted positives are correct, while recall measures how many actual positives are identified. Accuracy, on the other hand, is a general metric for classification tasks and is less suitable for object detection where class imbalance and localization are important.

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

"mAP evaluates object detection performance by considering both precision and recall across all classes, providing a balanced measure of detection accuracy and completeness."

ModelArts is built to support a variety of compute resources, including CPUs, GPUs, and Ascend AI processors. This heterogeneous resource pool allows users to select the hardware that best matches their training or inference requirements, ensuring cost efficiency and optimal performance for different workloads.

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

"ModelArts supports heterogeneous compute environments, enabling selection among CPUs, GPUs, and Ascend processors for flexible AI development."

Astandard normal distributionis a special case of the normal distribution with:

Mean (μ) = 0

Variance (σ²) = 1This standardization is widely used in statistics and machine learning to normalize features for improved model convergence. Statements A and B are incorrect because variance is never 0 in a valid distribution, and the mean is 0, not 1.

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

"The standard normal distribution is defined with μ = 0 and σ² = 1, providing a normalized scale for statistical analysis."

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."

An object detection system must:

Classifythe detected object (A).

Locatethe object by generating bounding box coordinates (C).

Estimate confidencescores indicating prediction reliability (D).

Object contour calculation (B) is a separate task often related toinstance segmentation, not general object detection.

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

"Object detection includes classification, bounding box localization, and confidence score prediction. Contour detection belongs to segmentation tasks."

MFCCs are a widely used feature extraction method in speech recognition. The process involves:

Converting the time-domain signal to the frequency domain using the Fourier transform.

Mapping the frequency scale to theMel scaleto mimic human hearing perception.

Taking the logarithm of the power spectrum to emphasize perceptually important differences.

Applying the discrete cosine transform (DCT) to obtaincepstral coefficients.

These steps capture the spectral envelope, which is important for distinguishing phonemes in speech.

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

"MFCCs transform audio to the Mel scale, applying log compression and cepstral transformation to align with human auditory characteristics."

The Seq2Seq (sequence-to-sequence) model converts an input sequence into an output sequence. In speech recognition, the input is a sequence of acoustic features, and the output is a sequence of text tokens. This is essentially aclassification taskbecause each output token is classified into a predefined vocabulary set. Although the output is sequential, each position in the output sequence involves a classification decision.

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

"In speech recognition, Seq2Seq models classify each output token from a fixed vocabulary, making the overall problem a sequence of classification tasks."

Historically, NLP has evolved through three main methodological phases:

Rule-based methods— used in early systems, relying on manually crafted grammar and lexicons.

Statistics-based methods— introduced probabilistic models such as HMMs and n-grams.

Deep learning-based methods— using neural networks, transformers, and embeddings.

A “recursion-based method” has never been recognized as a distinct NLP methodology, even though recursion can appear in linguistic theory, it is not a primary computational approach in NLP history.

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

"The evolution of NLP includes rule-based, statistical, and deep learning–based methods. Recursion-based approaches are not considered a formal method in NLP development history."

When text in scanned images is tilted,rotational transformationcan correct the angle of the text to align horizontally.Affine transformationcan correct tilt and skew by applying linear transformations such as rotation, scaling, and translation while preserving parallelism of lines. Perspective transformation (A) is used for correcting trapezoidal distortions, while grayscale transformation (B) only adjusts pixel intensity, not orientation.

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

"Text skew correction can be achieved using rotation and affine transformations, aligning text baselines and improving OCR accuracy."

Deep learning–based 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."