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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q37-Q42):
NEW QUESTION # 37
In the field of deep learning, which of the following activation functions has a derivative not greater than 0.5?
- A. ReLU
- B. Tanh
- C. SeLU
- D. Sigmoid
Answer: D
Explanation:
Thesigmoidactivation function maps inputs to the range (0, 1) and has a maximum derivative of 0.25 at x=0.
This derivative value is always # 0.5, making it the correct choice here. While sigmoid is historically used in neural networks, it suffers from the vanishing gradient problem for large positive or negative inputs due to its small derivative values. Other functions such as ReLU, Tanh, and SeLU have different derivative behaviors, with ReLU having a derivative of 1 for positive inputs, Tanh having derivatives up to 1, and SeLU designed for self-normalizing networks with derivatives potentially greater than 0.5.
Exact Extract from HCIP-AI EI Developer V2.5:
"Sigmoid compresses values into the (0,1) range, with its maximum derivative being 0.25, which is always less than 0.5." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Activation Functions in Neural Networks
NEW QUESTION # 38
In the deep neural network (DNN)-hidden Markov model (HMM), the DNN is mainly used for feature processing, while the HMM is mainly used for sequence modeling.
- A. FALSE
- B. TRUE
Answer: B
Explanation:
In hybridDNN-HMMspeech recognition:
* TheDNNacts as an acoustic model, transforming audio features into probability estimates for phonetic states.
* TheHMMmodels the temporal sequence and transitions between phonetic states, handling time dependencies and variability in speech.
This combination leverages the representational power of DNNs and the sequence modeling strengths of HMMs.
Exact Extract from HCIP-AI EI Developer V2.5:
"In DNN-HMM systems, the DNN outputs state posterior probabilities, and the HMM models the temporal sequence structure of speech." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Hybrid Speech Recognition Models
NEW QUESTION # 39
Transformer models outperform LSTM when analyzing and processing long-distance dependencies, making them more effective for sequence data processing.
- A. FALSE
- B. TRUE
Answer: B
Explanation:
Transformers, usingself-attention, can capture dependencies between any two positions in a sequence directly, regardless of distance. LSTMs, despite gating mechanisms, process sequences step-by-step and may struggle with very long dependencies due to vanishing gradients. This makes Transformers more efficient and accurate for tasks involving long-range context, such as document summarization or translation.
Exact Extract from HCIP-AI EI Developer V2.5:
"Transformers excel in modeling long-distance dependencies because self-attention relates all positions in a sequence simultaneously, unlike recurrent models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer vs. RNN Performance
NEW QUESTION # 40
John wants to deploy a large model locally to implement the Q&A assistant function for his company. Which of the following factors is unnecessary for John to consider?
- A. Output delay
- B. Demand for computing power
- C. Model development framework
- D. Model security
Answer: C
Explanation:
When deploying a pre-trained large model locally for a specific function, themodel development framework used during its creation is generally irrelevant unless modifications or retraining are required. However, John must consider:
* Output delay- to ensure low latency for real-time Q&A.
* Model security- to protect intellectual property and sensitive company data.
* Computing power demand- large models require high-performance hardware.
Exact Extract from HCIP-AI EI Developer V2.5:
"When deploying pre-trained models locally, the deployment plan should address computing resources, performance latency, and security, but does not require re-evaluating the original training framework." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Large Model Deployment Considerations
NEW QUESTION # 41
The image saturation can be enhanced by processing the ________ component of the HSV color space. (Enter H, S, or V.)
Answer:
Explanation:
S
Explanation:
In the HSV (Hue, Saturation, Value) color model:
* Hrepresents hue (color type).
* Srepresents saturation (color intensity or vividness).
* Vrepresents brightness.
To enhance saturation in an image, adjustments are made to theS component. Increasing S increases the color vividness, making the image appear more vibrant, while reducing S moves colors toward grayscale. This approach is widely used in image enhancement tasks, especially in object recognition and segmentation, where vivid colors improve feature contrast.
Exact Extract from HCIP-AI EI Developer V2.5:
"In HSV color space, saturation (S) describes the vividness of colors. Increasing the S value enhances saturation, making colors more intense, while decreasing it makes them closer to gray." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Processing Basics
NEW QUESTION # 42
......
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