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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q38-Q43):
NEW QUESTION # 38
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 # 39
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.
- A. FALSE
- B. TRUE
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 # 40
Which of the following are required for the image object detection algorithm?
- A. Confidence calculation
- B. Object contour calculation
- C. Object classification determination
- D. Object location calculation
Answer: A,C,D
Explanation:
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."
Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection Workflow
NEW QUESTION # 41
Which of the following are object detection algorithms?
- A. YOLO
- B. R-CNN
- C. SSD
- D. Faster-R-CNN
Answer: A,B,C,D
Explanation:
The major families of object detection algorithms include:
* R-CNN (Region-based CNN):Uses region proposals with CNN feature extraction.
* YOLO (You Only Look Once):Performs real-time detection by predicting bounding boxes and class probabilities in a single pass.
* SSD (Single Shot MultiBox Detector):Uses multiple feature maps for detecting objects at different scales in one pass.
* Faster-R-CNN:Improves R-CNN with a Region Proposal Network for speed.
Exact Extract from HCIP-AI EI Developer V2.5:
"Common object detection algorithms include R-CNN, Faster R-CNN, YOLO, and SSD, each using different approaches for balancing accuracy and speed." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection
NEW QUESTION # 42
What are the advantages of deep learning-based speech recognition algorithms?
- A. Forced alignment of annotated data
- B. End-to-end task processing
- C. Automated feature extraction
- D. No data training
Answer: B,C
Explanation:
Deep learning-based speech recognition offers two key advantages over traditional approaches:
* Automated feature extraction (B):Neural networks can directly learn features from raw or lightly processed audio without manual engineering of MFCCs or filter banks.
* End-to-end task processing (C):Models like CTC-based networks or attention-based architectures can map audio inputs directly to text outputs without intermediate models like GMM-HMM.
Options A and D are incorrect because forced alignment is part of traditional GMM-HMM systems, and deep learning still requires training with large datasets.
Exact Extract from HCIP-AI EI Developer V2.5:
"Deep learning models support automatic feature extraction and can implement end-to-end mapping from speech signals to text outputs." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: End-to-End Speech Recognition
NEW QUESTION # 43
......
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