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

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."

Overfitting occurs when a model learns the training data too well, including its noise and outliers, to the extent that it negatively impacts performance on unseen data. Contrary to the statement, overfitting is not caused by an “overly simple” model but typically by an overlycomplex modelwith too many parameters relative to the amount of training data. Such models have high variance and low bias, meaning they fit the training data perfectly but fail to generalize to new datasets. In the HCIP-AI EI Developer V2.5 curriculum, overfitting is described as a scenario where the model’s complexity captures random fluctuations in training data instead of general patterns, leading to poor predictive performance.

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

"Overfitting means that the trained model performs very well on the training dataset but poorly on new data. It usually results from excessive model complexity, insufficient data, or lack of regularization."

In themulti-head attentionmechanism:

A:True — the input embedding dimension is split across multiple heads, so each head operates on a lower-dimensional subspace before concatenation.

B:True — having multiple attention heads allows the model to attend to information from different representation subspaces simultaneously.

C:False — each head has its own learned linear transformations for queries, keys, and values.

D:False — after concatenation, the result is passed through a final linear projection, not fed back into the attention module directly.

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

"Multi-head attention divides the embedding dimension across heads to learn from multiple subspaces in parallel, then concatenates and linearly projects the result."