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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q60-Q65):

NEW QUESTION # 60
Which of the following statements about the functions of the encoder and decoder is true?

A. The encoder converts variable-length input sequences into fixed-length context vectors, encoding the information of the input sequences in the context vectors.
B. The output lengths of the encoder and decoder are the same.
C. The encoder converts context vectors into variable-length output sequences.
D. The decoder converts variable-length input sequences into fixed-length context vectors, encoding the information of the input sequences in the context vectors.

Answer: A

Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Encoder-Decoder Functions

NEW QUESTION # 61
In 2017, the Google machine translation team proposed the Transformer in their paperAttention is All You Need. In a Transformer model, there is customized LSTM with CNN layers.

A. TRUE
B. FALSE

Answer: B

Explanation:
TheTransformerarchitecture introduced in 2017 eliminates recurrence (RNN) and convolution entirely, relying solely on self-attention mechanisms and feed-forward layers. It does not contain LSTM or CNN components, which distinguishes it from previous sequence models.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Transformer architecture does not use RNNs or CNNs. It relies entirely on self-attention and feed- forward networks for sequence modeling." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Architecture Overview

NEW QUESTION # 62
In an HSV color space, H is for hue, S is for saturation, and V is for value. Which of the following statements about the HSV color space are true?

A. The HSV color space perceives colors differently from human eyes, so it is not suitable for image segmentation or color analysis.
B. Saturation describes how vivid the color is. The lower the saturation, the closer the color is to gray. The higher the saturation, the more vivid the color.
C. Hue indicates the basic color attributes, such as red, green, and blue.
D. Value is a measure of brightness. The image brightness can be enhanced by processing the V component of the HSV color space.

Answer: B,C,D

Explanation:
The HSV model separates chromatic content (Hue, Saturation) from brightness (Value):
* H (Hue):Defines the type of color (e.g., red, blue).
* S (Saturation):Measures vividness - low S means muted colors, high S means vivid colors.
* V (Value):Controls brightness - increasing V brightens the image.Contrary to option D, HSV aligns more closely with human perception than RGB, making itsuitablefor segmentation and color-based analysis.
Exact Extract from HCIP-AI EI Developer V2.5:
"HSV separates hue, saturation, and brightness, making it closer to human vision perception and suitable for color-based image analysis." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Color Spaces

NEW QUESTION # 63
Which of the following applications are supported by ModelArts ExeML?

A. Predictive maintenance of manufacturing equipment
B. Automatic offering classification
C. Dress code conformance monitoring in campuses
D. Anomalous sound detection in production or security scenarios

Answer: A,B,C,D

Explanation:
ModelArtsExeML(Expert Experience Machine Learning) enables users without programming expertise to build AI models through a visual interface. It supports multiple application scenarios, including:
* Predictive maintenance in manufacturing to detect potential equipment failures.
* Monitoring compliance with dress codes in school or workplace settings.
* Detecting unusual sounds in manufacturing or security contexts.
* Classifying offerings automatically in e-commerce or retail systems.
Exact Extract from HCIP-AI EI Developer V2.5:
"ModelArts ExeML supports intelligent applications in industrial maintenance, campus security, sound anomaly detection, and automated product classification." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: ModelArts ExeML Application Scenarios

NEW QUESTION # 64
Maximum likelihood estimation (MLE) requires knowledge of the sample data's distribution type.

A. FALSE
B. TRUE

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 # 65
......

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q60-Q65):

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