Huawei H13-321_V2.5 Exam With Confidence Using Practice Dumps

In Transformers:

Residual connectionshelp preserve gradient flow through deep networks, mitigating vanishing/exploding gradient issues.

Layer normalizationstabilizes training by normalizing across features, improving convergence speed and training stability.Thus,Ais correct, while B, C, and D are incorrect.

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

"Residual connections and layer normalization stabilize deep network training, prevent gradient issues, and accelerate convergence."

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."