Which tool accelerates data exploration and cleansing in Fabric?

• A. Dataflows
• B. Data Wrangler
• C. Lakehouse
• D. Datasets

Answer: (B)

Data exploration and cleansing in Fabric are sped up by a tool built specifically for hands-on data wrangling. Data Wrangler is designed to let you visually inspect your data, spot quality issues, and apply a sequence of transformations—such as filtering, renaming, deduplicating, and type conversions—with immediate feedback. This interactive, step-by-step approach makes it quick to experiment with different cleaning strategies, see their effects, and reuse successful transformations, which dramatically accelerates the prep work before analysis or modeling.

Dataflows, while capable of building repeatable ETL pipelines, isn’t focused on the rapid, ad-hoc exploration and cleansing workflow you get with a dedicated wrangling tool. Lakehouse represents the storage layer that holds the data, not the interactive data cleaning process. Datasets are containers for data used in analyses, not the tooling that performs cleansing and exploration.