Free Databricks Certified Data Analyst Associate Actual Exam Questions - Question 3 Discussion

A seems off since data quality testing usually needs data engineering tools, not just SQL. E looks like orchestration, so probably not Databricks SQL either. Does that narrow it down?

Makes sense to pick C since Databricks SQL is designed for querying and merging data sources directly. Tasks like data quality checks or ML tracking usually need different parts of the platform. C

C, since Databricks SQL is mainly for data querying and combining sources.

C imo, because Databricks SQL is mainly for querying and merging datasets, so combining sources fits best. The others involve more pipeline or ML tasks outside pure SQL work.

Maybe C makes the most sense here because Databricks SQL is all about querying and combining data, which fits merging two sources. A is more about data validation, which might need other tools. B and D involve machine learning concepts, so they probably go beyond SQL. E sounds like workflow automation, so that’s more for orchestration tools. So yeah, C stands out as the project best suited for Databricks SQL.

C/D? Combining data sources (C) definitely suits Databricks SQL since it’s mainly for querying and data prep. Clustering customers (D) usually involves ML libraries beyond SQL, so that seems off. Plus, automating workflows (E) needs orchestration tools, and testing data quality (A) is more ETL or validation scripts. Tracking feature usage (B) sounds like ML metadata tracking, not a pure SQL task. So yeah, C fits best because SQL handles data integration smoothly, while the rest lean into specialized ML or pipeline work.

Maybe D fits less since clustering algorithms usually need ML frameworks, not just SQL queries. A and E seem more about building or automating pipelines, which isn’t SQL’s main job. B is about tracking features, which sounds more like ML lifecycle stuff. So by process of elimination, C makes the most sense here—it’s about combining data sources, which SQL is designed to handle easily in Databricks.

Definitely not A, B, D, or E because those are more about data engineering, ML, or orchestration. Databricks SQL is designed for straightforward querying and combining datasets, so C fits perfectly. It’s all about creating a unified view of the data, which SQL handles best. The others require other tools or environments within Databricks.

Option C makes the most sense since Databricks SQL is optimized for querying and joining datasets. The others involve either ML tasks or pipeline automation, which don’t fit SQL’s core strengths.

Isn’t A more about data engineering pipelines than SQL querying?

C imo, since Databricks SQL is mainly for querying and combining data sets. Options like A or B involve data quality checks or ML features, which aren't really SQL's strong suit.

I think the answer is C. Databricks SQL is good for combining and querying data from different sources, so merging datasets fits well here. The others seem more related to ML or workflow automation. Does that make sense?