Pass the Huawei HCIP-AI EI Developer H13-321_V2.5 Questions and answers with ExamsMirror

Astandard normal distributionis a special case of the normal distribution with:

Mean (μ) = 0

Variance (σ²) = 1This standardization is widely used in statistics and machine learning to normalize features for improved model convergence. Statements A and B are incorrect because variance is never 0 in a valid distribution, and the mean is 0, not 1.

Exact Extract from HCIP-AI EI Developer V2.5:

"The standard normal distribution is defined with μ = 0 and σ² = 1, providing a normalized scale for statistical analysis."

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."

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."

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."

The convolutional layer in CNNs is optimized for spatial feature extraction:

Local connectivity(A) reduces computation and memory usage.

Parameter sharing(B) reduces the number of learnable parameters and helps prevent overfitting.

Stride control(C) allows adjusting the output resolution and computational cost.

Sliding kernel operation(D) extracts local patterns without manual feature definition.

Exact Extract from HCIP-AI EI Developer V2.5:

"CNN convolutional layers leverage local connectivity, parameter sharing, and stride control to efficiently extract local features, reducing computational requirements compared to fully-connected layers."

FastTextis an extension of Word2Vec developed by Facebook AI Research. Unlike Word2Vec, which learns embeddings for whole words, FastText represents each word as a sum of its character n-gram embeddings. This helps in handling rare words and morphologically rich languages by generating embeddings for unseen words from their subword components.

Exact Extract from HCIP-AI EI Developer V2.5:

"FastText decomposes words into character n-grams and represents words as the sum of their n-gram vectors, improving representation for rare and out-of-vocabulary words."

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."

ModelArtsExeML(Expert Experience Machine Learning) enables users without programming expertise to build AI models through a visual interface. It supports multiple application scenarios, including:

Predictive maintenance in manufacturing to detect potential equipment failures.

Monitoring compliance with dress codes in school or workplace settings.

Detecting unusual sounds in manufacturing or security contexts.

Classifying offerings automatically in e-commerce or retail systems.

Exact Extract from HCIP-AI EI Developer V2.5:

"ModelArts ExeML supports intelligent applications in industrial maintenance, campus security, sound anomaly detection, and automated product classification."