The image collection is an unstructured, unlabeled dataset .
First, image files are normally classified as unstructured data because their contents are not represented according to a fixed tabular schema consisting of defined rows and columns. AWS data architecture guidance explicitly identifies images as examples of unstructured data. AWS describes unstructured data as information that does not conform to a predefined data model and is typically stored as individual files.
Second, the data is unlabeled because none of the images contains an annotation or target specifying the land-cover class represented by that image. The scenario states that there is no metadata indicating whether an image represents forest, water, agricultural land, urban land, desert terrain, or another classification.
AWS guidance on machine-learning datasets distinguishes supervised labeled datasets from unlabeled inputs. For example, SageMaker Canvas requires image labels when training a single-label image prediction model and instructs users to assign labels to images that are currently unlabeled.
A structured, labeled dataset , option A, would typically contain defined attributes or fields together with known target labels.
A semi-structured dataset , option C, might include data such as JSON or XML that does not use a strict relational schema but contains explicit structural elements or identifiers. Raw images do not fit that definition merely because they use different image file formats.
Option D correctly says unlabeled but incorrectly describes the data as structured.
For supervised land classification, the mining company would typically need to create ground-truth annotations that associate images or image regions with their correct land-cover classes. AWS services such as SageMaker Ground Truth can support data-labeling workflows when supervised training data must be produced at scale.
Because the company currently possesses raw images without class annotations, the dataset is unequivocally B. Unstructured, unlabeled dataset .
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