CERTIFIED-DATA-ANALYST-ASSOCIATE Question #75: Real Exam Question with Answer & Explanation

Question

Which example of data projects represents a common analytics application to be completed in Databricks SQL?

Options

• AAugmenting gold-layer tables with additional external information
• BAutomating complex notebook-based workflows with multiple tasks
• CTesting the quality of data as it is imported from a source
• DSegmenting customers into like groups using a clustering algorithm

Explanation

Databricks SQL is purpose-built for SQL-based analytics on structured data, making it ideal for querying and enriching gold-layer tables - the clean, business-ready layer of the medallion architecture - with additional external data sources. Option B describes orchestrating multi-task notebook workflows, which is the domain of Databricks Workflows (Jobs), not SQL. Option C refers to data quality testing during ingestion, which belongs to the bronze/silver layer processing handled in notebooks or Delta Live Tables pipelines, not a SQL analytics workspace. Option D describes machine learning clustering, which requires ML libraries and compute best suited to Databricks Notebooks with MLlib or scikit-learn, not SQL.

Memory tip: Think of Databricks SQL as the "boardroom" layer - analysts arrive after the data is already cleaned and want to query, join, and enrich it for dashboards and reports. If the task sounds like engineering (pipelines, testing, orchestration) or ML, it lives elsewhere.

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