Free Huawei H13-321_V2.5 Practice Exam with Questions & Answers | Set: 2

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."

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."

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."

Overfitting occurs when a model learns the training data too well, including its noise and outliers, to the extent that it negatively impacts performance on unseen data. Contrary to the statement, overfitting is not caused by an “overly simple” model but typically by an overlycomplex modelwith too many parameters relative to the amount of training data. Such models have high variance and low bias, meaning they fit the training data perfectly but fail to generalize to new datasets. In the HCIP-AI EI Developer V2.5 curriculum, overfitting is described as a scenario where the model’s complexity captures random fluctuations in training data instead of general patterns, leading to poor predictive performance.

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

"Overfitting means that the trained model performs very well on the training dataset but poorly on new data. It usually results from excessive model complexity, insufficient data, or lack of regularization."

In Transformers:

Residual connectionshelp preserve gradient flow through deep networks, mitigating vanishing/exploding gradient issues.

Layer normalizationstabilizes training by normalizing across features, improving convergence speed and training stability.Thus,Ais correct, while B, C, and D are incorrect.

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

"Residual connections and layer normalization stabilize deep network training, prevent gradient issues, and accelerate convergence."

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."

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."

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."