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H13-321_V2.5 Questions & Answers & H13-321_V2.5 Study Guide & H13-321_V2.5 Exam Preparation

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q19-Q24):

NEW QUESTION # 19
Maximum likelihood estimation (MLE) can be used for parameter estimation in a Gaussian mixture model (GMM).

A. TRUE
B. FALSE

Answer: A

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 # 20
Among image preprocessing techniques, gamma correction is a common non-linear brightness adjustment method. Which of the following statements are true about the application and features of gamma correction?

A. Gamma correction applies only to grayscale images and does not apply to color images.
B. When # < 1, the input high grayscale range is compressed, and the low grayscale range is stretched, enhancing the dark areas while compressing the bright areas.
C. When # > 1, the input low grayscale range is compressed, and the high grayscale range is stretched, enhancing the bright areas while compressing the dark areas.
D. Gamma correction is an enhancement technique based on exponential transformation mapping. It is used for non-linear contrast stretching.

Answer: B,C,D

Explanation:
Gamma correction is anon-linearimage processing method used to adjust brightness and contrast. It is not limited to grayscale images - it can be applied to both grayscale and color images by operating on individual channels.
* # < 1:Enhances dark regions (brightens shadows) and compresses highlights.
* # > 1:Enhances bright regions and compresses dark regions.It is based onpower-law (exponential) transformation, making it effective for adjusting human-perceived luminance.
Exact Extract from HCIP-AI EI Developer V2.5:
"Gamma correction is a non-linear brightness adjustment based on power-law transformation. It applies to both grayscale and color images. For #<1, dark regions are brightened; for #>1, bright regions are enhanced." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Image Enhancement

NEW QUESTION # 21
Which of the following is not an algorithm for training word vectors?

A. Word2Vec
B. FastText
C. TextCNN
D. BERT

Answer: C

Explanation:
* Word2VecandFastTextare neural network-based algorithms designed for generating dense vector representations of words.
* BERTis a transformer-based language model that also generates contextualized word embeddings.
* TextCNN, however, is a text classification model, not a word vector training algorithm. It uses convolutional neural networks to extract features from already vectorized text but does not learn static word embeddings in the same sense as Word2Vec or FastText.
Exact Extract from HCIP-AI EI Developer V2.5:
"Word2Vec, FastText, and BERT can be used to train word embeddings. TextCNN is a classification model that uses embeddings but does not train them as its primary function." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Word Vector Representation

NEW QUESTION # 22
Which of the following are the impacts of the development of large models?

A. The accuracy and efficiency of natural language processing tasks will improve
B. Large models will completely replace small and domain-specific models
C. Model pre-training costs will be reduced
D. Data privacy and security issues will be exacerbated

Answer: A,D

Explanation:
The emergence of large AI models (e.g., GPT, Pangu, BERT) has led to:
* C:Improved accuracy and efficiency in NLP and other AI tasks because of their ability to capture deep semantic and contextual information.
* D:Increased data privacy and security concerns, as large models require massive datasets which may contain sensitive or proprietary information.Ais false - large models increase pre-training costs.Bis false - small and domain-specific models still play important roles due to efficiency and deployment constraints.
Exact Extract from HCIP-AI EI Developer V2.5:
"Large models improve task performance but raise privacy and security concerns. They do not necessarily reduce training cost or eliminate the need for smaller models." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Large Model Trends and Challenges

NEW QUESTION # 23
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. Demand for computing power
B. Output delay
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 # 24
......

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