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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q46-Q51):

NEW QUESTION # 46
Which of the following ModelArts training parameters is used to customize hyperparameters?

A. Compute Nodes
B. Resource Pool
C. Algorithm Type
D. Hyperparameter

Answer: D

Explanation:
In Huawei Cloud ModelArts training jobs, theHyperparameterparameter is explicitly designed to allow users to define custom training settings, such as learning rate, batch size, and number of epochs.
* Algorithm Typespecifies the model algorithm.
* Resource Poolselects the computational environment.
* Compute Nodesdetermines the number of nodes used for training.
Exact Extract from HCIP-AI EI Developer V2.5:
"The Hyperparameter field in ModelArts allows users to define and pass custom training parameters to the algorithm for tuning performance." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: ModelArts Training Job Parameters

NEW QUESTION # 47
Which of the following is not an acoustic feature of speech?

A. Duration
B. Frequency
C. Amplitude
D. Semantics

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 # 48
Vision transformer (ViT) performs well in image classification tasks. Which of the following is the main advantage of ViT?

A. It can handle small datasets with minimal labeling required.
B. The self-attention mechanism is used to capture global features of images, improving classification accuracy.
C. It can process high-resolution images to enhance classification accuracy.
D. It achieves fast convergence without using pre-trained models.

Answer: B

Explanation:
TheVision Transformer (ViT)applies the transformer architecture to image patches. Its key advantage is the use ofself-attentionto capture global dependencies and relationships between all parts of an image. This allows ViT to excel in classification accuracy, especially on large datasets with sufficient pre-training.
Exact Extract from HCIP-AI EI Developer V2.5:
"ViT applies self-attention to image patches, enabling global feature extraction and improving classification performance compared to local receptive fields in CNNs." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Models in Vision

NEW QUESTION # 49
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. Model development framework
B. Demand for computing power
C. Model security
D. Output delay

Answer: A

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 # 50
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).

A. FALSE
B. TRUE

Answer: B

Explanation:
A Gaussian mixture model represents a probability distribution as a weighted sum of multiple Gaussian components. TheMLEmethod can be applied to estimate the parameters of these components (means, variances, and mixing coefficients) by maximizing the likelihood of the observed data. The Expectation- Maximization (EM) algorithm is typically used to perform MLE in GMMs because it can handle hidden (latent) variables representing the component assignments.
Exact Extract from HCIP-AI EI Developer V2.5:
"MLE, implemented through the EM algorithm, is commonly used to estimate the parameters of Gaussian mixture models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Gaussian Mixture Models

NEW QUESTION # 51
......

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