H13-321_V2.5 HCIP - AI EI Developer V2.5 Exam Questions and Answers

The standardattention mechanisminvolves:

Computing attention scores via the dot product of query and key vectors (A).

Applying a normalization function (typically softmax) to obtain attention weights (D).

Using these weights to compute a weighted sum of the value vectors (B).OptionCis not a standard step — non-linear mappings are not applied after the weighted sum in the basic attention formula.

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

"Attention computes dot products between query and key, normalizes scores with softmax, and uses them to weight value vectors."

Text CNN applies convolutional layers directly to text data represented as word embeddings. By using multiple kernel sizes, Text CNN captures features from n-grams of varying lengths. These features are pooled and passed to fully connected layers for classification tasks such as sentiment analysis or spam detection.

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

"Text CNN applies convolution and pooling over word embeddings to extract local features for text classification."

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."

When deploying a pre-trained large model locally for a specific function, themodel development frameworkused during its creation is generally irrelevant unless modifications or retraining are required. However, John must consider:

Output delay— to ensure low latency for real-time Q&A.

Model security— to protect intellectual property and sensitive company data.

Computing power demand— large models require high-performance hardware.

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

"When deploying pre-trained models locally, the deployment plan should address computing resources, performance latency, and security, but does not require re-evaluating the original training framework."

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."

Thesigmoidactivation function maps inputs to the range (0, 1) and has a maximum derivative of 0.25 at x=0. This derivative value is always ≤ 0.5, making it the correct choice here. While sigmoid is historically used in neural networks, it suffers from the vanishing gradient problem for large positive or negative inputs due to its small derivative values. Other functions such as ReLU, Tanh, and SeLU have different derivative behaviors, with ReLU having a derivative of 1 for positive inputs, Tanh having derivatives up to 1, and SeLU designed for self-normalizing networks with derivatives potentially greater than 0.5.

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

"Sigmoid compresses values into the (0,1) range, with its maximum derivative being 0.25, which is always less than 0.5."

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."

A Gaussian mixture model represents a probability distribution as a weighted sum of multiple Gaussian components. TheMLEmethod can be applied to estimate the parameters of these components (means, variances, and mixing coefficients) by maximizing the likelihood of the observed data. The Expectation-Maximization (EM) algorithm is typically used to perform MLE in GMMs because it can handle hidden (latent) variables representing the component assignments.

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

"MLE, implemented through the EM algorithm, is commonly used to estimate the parameters of Gaussian mixture models."

In Huawei Cloud ModelArts training jobs, theHyperparameterparameter is explicitly designed to allow users to define custom training settings, such as learning rate, batch size, and number of epochs.

Algorithm Typespecifies the model algorithm.

Resource Poolselects the computational environment.

Compute Nodesdetermines the number of nodes used for training.

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

"The Hyperparameter field in ModelArts allows users to define and pass custom training parameters to the algorithm for tuning performance."

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."

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 isnotsupported 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."

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."

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."

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."