Exam H13-321_V2.5 Simulations | Passing H13-321_V2.5 Score

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Huawei HCIP-AI-EI Developer V2.5 Sample Questions (Q55-Q60):

NEW QUESTION # 55
Which of the following are required for the image object detection algorithm?

A. Object contour calculation
B. Object classification determination
C. Confidence calculation
D. Object location calculation

Answer: B,C,D

Explanation:
An object detection system must:
* Classifythe detected object (A).
* Locatethe object by generating bounding box coordinates (C).
* Estimate confidencescores indicating prediction reliability (D).
Object contour calculation (B) is a separate task often related toinstance segmentation, not general object detection.
Exact Extract from HCIP-AI EI Developer V2.5:
"Object detection includes classification, bounding box localization, and confidence score prediction.
Contour detection belongs to segmentation tasks."
Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Object Detection Workflow

NEW QUESTION # 56
What are the adjacency relationships between two pixels whose coordinates are (21,13) and (22,12)?

A. No adjacency relationship
B. 4-adjacency
C. Diagonal adjacency
D. 8-adjacency

Answer: C,D

Explanation:
Pixel adjacency describes how pixels are connected:
* 4-adjacency:Pixels share a side (up, down, left, right).
* Diagonal adjacency:Pixels touch at a corner.
* 8-adjacency:Combination of 4-adjacency and diagonal adjacency.
Given coordinates (21,13) and (22,12), the pixels differ by 1 in both x and y directions, meaning they meet at a corner - this isdiagonal adjacency. Since 8-adjacency includes both side and diagonal adjacency, they are also8-adjacent.
Exact Extract from HCIP-AI EI Developer V2.5:
"In 8-adjacency, pixels are considered neighbors if they are connected horizontally, vertically, or diagonally.
Diagonal adjacency occurs when pixels touch at a corner."
Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Digital Image Basics

NEW QUESTION # 57
Which of the following statements about the multi-head attention mechanism of the Transformer are true?

A. The multi-head attention mechanism captures information about different subspaces within a sequence.
B. Each header's query, key, and value undergo a shared linear transformation to obtain them.
C. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
D. The concatenated output is fed directly into the multi-headed attention mechanism.

Answer: A,C

NEW QUESTION # 58
Which of the following statements about the multi-head attention mechanism of the Transformer are true?

A. The multi-head attention mechanism captures information about different subspaces within a sequence.
B. Each header's query, key, and value undergo a shared linear transformation to obtain them.
C. The dimension for each header is calculated by dividing the original embedded dimension by the number of headers before concatenation.
D. The concatenated output is fed directly into the multi-headed attention mechanism.

Answer: A,C

Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Transformer Multi-Head Attention

NEW QUESTION # 59
In natural language processing tasks, word vector evaluation is an important aspect for measuring the performance of a word embedding model. Which of the following statements about word vector evaluation are true?

A. The word analogy task evaluates the capability of word vectors in capturing semantic relationships between words, for example, by determining whether "king - man + woman = ?" is close to "queen".
B. Extrinsic evaluation is the main method used for evaluating word vectors because it directly reflects the performance of word vectors in real-world application tasks.
C. Word vector evaluation can be performed through intrinsic evaluation. Common methods include word similarity tasks and word analogy tasks.
D. Word similarity tasks typically employ manually labeled datasets to evaluate word vectors, compute the cosine similarity between word vectors, and compare it with the manual labeling result.

Answer: A,C,D

Explanation:
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." Reference:HCIP-AI EI Developer V2.5 Official Study Guide - Chapter: Word Vector Evaluation

NEW QUESTION # 60
......

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