Huawei H13-321_V2.5 - HCIP - AI EI Developer V2.5 Exam

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."

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."

The standardattention mechanisminvolves:

Computing attention scores via the dot product of query and key vectors (A).

Applying a normalization function (typically softmax) to obtain attention weights (D).

Using these weights to compute a weighted sum of the value vectors (B).OptionCis not a standard step — non-linear mappings are not applied after the weighted sum in the basic attention formula.

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

"Attention computes dot products between query and key, normalizes scores with softmax, and uses them to weight value vectors."

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."

Traditional word representations like one-hot vectors are sparse and orthogonal, failing to capture semantic similarities.Distributed semantic representations(word embeddings) map words to dense, continuous vectors in a multi-dimensional space where similar words have similar vector representations. This approach enables better generalization and semantic reasoning in NLP tasks.

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

"Distributed semantic representation maps words to dense real-valued vectors in continuous space, allowing semantic similarity to be captured in vector geometry."

Maximum likelihood estimation is a statistical method for estimating parameters of a probability distribution by maximizing the likelihood function. To apply MLE, theform of the probability distribution(e.g., normal, exponential) must be known in advance because the likelihood function is defined based on this distribution. Without knowing the distribution type, the estimation process cannot be properly formulated.

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

"MLE assumes that the underlying probability distribution type of the sample data is known and uses it to construct the likelihood function for parameter estimation."

The Transformer model architecture includes:

Encoder:Encodes the input sequence into contextualized representations.

Decoder:Uses the encoder output and self-attention over previously generated tokens to produce the target sequence.

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

"The Transformer consists of an encoder-decoder structure, with self-attention mechanisms in both components for sequence-to-sequence learning."

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."

Transformers are designed for sequence modeling without recurrence or convolution.

A:True — self-attention captures global dependencies efficiently, outperforming RNNs/CNNs in long text processing.

B:True — multi-head attention computes multiple attention projections in parallel.

C:True — the architecture is purely attention-based.

D:False — positional encoding isrequiredbecause self-attention does not inherently encode sequence order.

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

"The Transformer uses self-attention to model dependencies and multi-head attention to capture features in different subspaces. Positional encoding must be added to preserve sequence order."