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

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."

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."

Word vector evaluation can be:

Intrinsic:Directly tests vector properties via word similarity and analogy tasks.

Extrinsic:Tests in downstream applications.

A:True — word similarity tasks use human-labeled datasets and cosine similarity.

B:True — intrinsic evaluations include similarity and analogy tasks.

C:True — analogy tests assess how well vectors capture semantic relationships.

D:False — both intrinsic and extrinsic methods are valuable, but intrinsic methods are more common for initial evaluations.

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

"Intrinsic evaluations (similarity, analogy) test embedding quality directly, while extrinsic evaluations measure impact on real tasks."