H13-321_V2.5 Practice Questions

NER(Named Entity Recognition) is a core NLP task that involves locating and categorizing entities within text into predefined categories like persons, organizations, places, dates, monetary values, and percentages. NER is widely used in information extraction, question answering, and knowledge graph construction.

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

"NER identifies and classifies named entities in text into categories such as person names, organizations, locations, time expressions, and numeric values."

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."

In Python'sjiebalibrary, the cut() method is used for Chinese word segmentation. It splits a given sentence into individual words based on probabilistic models and a dictionary. The method supports both precise mode and full mode, with precise mode being the default for balanced accuracy and completeness.

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

"The jieba.cut() method segments Chinese text into words, supporting multiple modes for different application needs."

ModelArts provides an integrated environment for data labeling, model training, deployment, and management, supporting various AI application scenarios:

Image classificationfor categorizing visual content.

Object detectionfor locating and identifying multiple objects in images or video frames.

Speech recognitionfor converting speech to text.

Video analyticsfor automated video content analysis.

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

"ModelArts supports a wide range of AI tasks including image classification, object detection, speech recognition, and intelligent video analytics."

The HSV model separates chromatic content (Hue, Saturation) from brightness (Value):

H (Hue):Defines the type of color (e.g., red, blue).

S (Saturation):Measures vividness — low S means muted colors, high S means vivid colors.

V (Value):Controls brightness — increasing V brightens the image.Contrary to option D, HSV aligns more closely with human perception than RGB, making itsuitablefor segmentation and color-based analysis.

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

"HSV separates hue, saturation, and brightness, making it closer to human vision perception and suitable for color-based image analysis."