Data cleansing fixes errors and inconsistent values while data transformation converts and restructures data to meet the target schema or analytic needs is the correct option.

Data cleansing is focused on improving data quality by removing duplicates and correcting typos and inconsistent values and by handling missing or invalid entries so analyses are based on accurate inputs.

Data transformation reshapes and converts data so it fits the target schema and the analytic requirements and this includes tasks such as type conversion aggregation joining and pivoting to produce the structures that downstream tools expect.

Data cleansing is applied only to structured datasets while data transformation is used for unstructured data is incorrect because both cleansing and transformation can apply to structured and unstructured data and the distinction is about purpose rather than data format.

BigQuery is incorrect because it is not an explanation of the difference between cleansing and transformation and it is also a cloud data warehouse product that is unrelated to the conceptual distinction asked for in this question.

Data cleansing always occurs before ingestion and data transformation always takes place after ingestion is incorrect because cleansing and transformation can occur at multiple stages of a pipeline and the timing depends on architecture and requirements rather than a fixed order.

Full AWS Practitioner Certification Question