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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q42-Q47):
NEW QUESTION # 42
What are the adjacency relationships between two pixels whose coordinates are (21,13) and (22,12)?
Answer: A,D
Explanation:
Pixel adjacency describes how pixels are connected:
* 4-adjacency:Pixels share a side (up, down, left, right).
* Diagonal adjacency:Pixels touch at a corner.
* 8-adjacency:Combination of 4-adjacency and diagonal adjacency.
Given coordinates (21,13) and (22,12), the pixels differ by 1 in both x and y directions, meaning they meet at a corner - this isdiagonal adjacency. Since 8-adjacency includes both side and diagonal adjacency, they are also8-adjacent.
Exact Extract from HCIP-AI EI Developer V2.5:
"In 8-adjacency, pixels are considered neighbors if they are connected horizontally, vertically, or diagonally.
Diagonal adjacency occurs when pixels touch at a corner."
Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Digital Image Basics
NEW QUESTION # 43
Maximum likelihood estimation (MLE) requires knowledge of the sample data's distribution type.
Answer: B
Explanation:
Maximum likelihood estimation is a statistical method for estimating parameters of a probability distribution by maximizing the likelihood function. To apply MLE, theform of the probability distribution(e.g., normal, exponential) must be known in advance because the likelihood function is defined based on this distribution.
Without knowing the distribution type, the estimation process cannot be properly formulated.
Exact Extract from HCIP-AI EI Developer V2.5:
"MLE assumes that the underlying probability distribution type of the sample data is known and uses it to construct the likelihood function for parameter estimation." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Statistical Parameter Estimation
NEW QUESTION # 44
Mel-frequency cepstral coefficients (MFCCs) take into account human auditory characteristics by first mapping the linear spectrum to the Mel nonlinear spectrum based on auditory perception, and then converting it to the cepstral domain.
Answer: B
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Speech Feature Extraction
NEW QUESTION # 45
Which of the following is not an acoustic feature of speech?
Answer: D
Explanation:
In speech signal processing,acoustic featuresdescribe measurable physical properties of sound waves, such as duration(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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Speech Feature Extraction
NEW QUESTION # 46
The technologies underlying ModelArts support a wide range of heterogeneous compute resources, allowing you to flexibly use the resources that fit your needs.
Answer: B
Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: ModelArts Infrastructure
NEW QUESTION # 47
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