A data team has been given a series of projects by a consultant that need to be implemented in the Databricks Lakehouse Platform. Which of the…

Databricks SQL is the dedicated workspace within the Databricks Lakehouse Platform for data analysts to execute SQL queries, create visualizations, and build dashboards. A core task for any data analyst is to combine different data sources to create a unified view for analysis. This is fundamentally accomplished using SQL JOIN operations. Therefore, combining two data sources into a single, comprehensive dataset is a project perfectly suited for the Databricks SQL query editor, aligning with its primary purpose of enabling business intelligence and SQL-based analytics.

A. Testing the quality of data as it is imported from a source: This is a data engineering task, often automated in pipelines using tools like Delta Live Tables, rather than a primary project for an analyst in Databricks SQL.

B. Tracking usage of feature variables for machine learning projects: This is a machine learning operations (MLOps) task performed in the Data Science & Engineering workspace, typically using MLflow, not Databricks SQL.

D. Segmenting customers into like groups using a clustering algorithm: This is a data science and machine learning task that requires ML libraries and is executed in notebooks within the Data Science & Engineering workspace.

E. Automating complex notebook-based workflows with multiple tasks: This is the specific function of Databricks Workflows, which is used to orchestrate and schedule complex data pipelines, not the Databricks SQL environment itself.

1. Databricks Documentation

"What is Databricks SQL?": "Databricks SQL (DB SQL) is a serverless data warehouse on the Databricks Lakehouse Platform that lets you run all your SQL and BI applications at scale... It empowers a new generation of data analysts who work directly on the same data used for ETL

data science

and ML." This source establishes that Databricks SQL is for analysts performing SQL and BI tasks

such as querying and combining data (Option C).

2. Databricks Documentation

"Get started: Run and save a query": This guide demonstrates the primary function of the Databricks SQL UI

which is to write and execute SQL queries

such as SELECT statements with JOIN clauses

to explore and combine data. This directly supports the action described in Option C.

3. Databricks Documentation

"Introduction to Databricks machine learning": "Databricks Machine Learning is an integrated end-to-end machine learning platform incorporating managed services for experiment tracking

model training

feature development and management

and feature and model serving." This confirms that tasks like feature tracking (Option B) and clustering algorithms (Option D) belong to the ML persona and workspace.

4. Databricks Documentation

"Introduction to Databricks Workflows": "Databricks Workflows orchestrates data processing

machine learning

and analytics pipelines on the Databricks Lakehouse Platform. You can create and run a job that consists of a single task or a large

multi-task workflow with complex dependencies." This clearly defines the purpose of Workflows

making it the correct tool for the task in Option E.