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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q26-Q31):

NEW QUESTION # 26
The U-Net uses an upsampling mechanism and has a fully-connected layer.

A. TRUE
B. FALSE

Answer: B

Explanation:
U-Net is a convolutional neural network architecture designed for biomedical image segmentation. It consists of a contracting path for feature extraction and an expansive path for precise localization, usingupsamplingin the decoding path. However, U-Netdoes not include fully-connected layers; instead, it uses only convolutional layers to maintain spatial information. Removing fully-connected layers ensures the network can handle images of varying sizes without requiring fixed input dimensions.
Exact Extract from HCIP-AI EI Developer V2.5:
"U-Net architecture is fully convolutional and avoids fully-connected layers to preserve spatial resolution, relying on upsampling in the decoder path for segmentation tasks." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Semantic Segmentation Networks

NEW QUESTION # 27
When the chi-square test is used for feature selection, SelectKBest and _____ function or class must be imported from the sklearn.feature_selection module. (Enter the function interface name.) chi2 Explanation:
In feature selection for classification tasks, thechi-square (#²)statistical test can be applied to evaluate the independence between features and target labels.
In Python's scikit-learn library, this is implemented using:

Answer:

Explanation:
python
CopyEdit
from sklearn.feature_selection import SelectKBest, chi2
SelectKBest selects the top K features based on scores returned by the chi2 function.
Exact Extract from HCIP-AI EI Developer V2.5:
"In scikit-learn, SelectKBest with chi2 can be used for feature selection by scoring features according to the chi-square statistic." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Feature Selection Methods

NEW QUESTION # 28
The deep neural network (DNN)-hidden Markov model (HMM) does not require the HMM-Gaussian mixture model (GMM) as an auxiliary.

A. TRUE
B. FALSE

Answer: B

Explanation:
In traditional hybridDNN-HMMspeech recognition systems, the DNN is often trained usingframe-level alignmentsgenerated by anHMM-GMMsystem. The GMM serves as an auxiliary tool to perform initial alignments between audio frames and phonetic units, which are then used to train the DNN. Without the HMM-GMM step, supervised training of the DNN in this context is typically not possible.
Exact Extract from HCIP-AI EI Developer V2.5:
"In a DNN-HMM hybrid system, the DNN replaces the GMM in modeling emission probabilities, but GMMs are still used in the initial alignment process to prepare training data for the DNN." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Hybrid Speech Recognition Models

NEW QUESTION # 29
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 # 30
Seq2Seq is a model that translates one sequence into another sequence, essentially consisting of two recurrent neural networks (RNNs), one is the Encoder, and the other is the ---------. (Fill in the blank.)

Answer:

Explanation:
Decoder
Explanation:
The Seq2Seq architecture is widely used in machine translation, speech recognition, and other NLP tasks. It consists of:
* Encoder:Processes the input sequence and encodes it into a fixed-length context vector containing semantic information.
* Decoder:Uses this context vector to generate the target output sequence step by step.
Exact Extract from HCIP-AI EI Developer V2.5:
"Seq2Seq models consist of an encoder and a decoder. The encoder transforms the input into a context vector, which the decoder uses to generate the output sequence." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Encoder-Decoder Architecture

NEW QUESTION # 31
......

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