Azure Databricks Data Engineering Workflow: 10 practice questions

Question 1 of 10

Which Lakeflow component provides connectors for ingesting data from databases, applications, files, and streams?

1. ADatabricks Runtime
2. BLakeflow Jobs
3. CLakeflow Connect
4. DLakeflow pipelines

Question 2 of 10

What can Delta Lake schema enforcement detect when raw files are converted to Delta tables?

1. AFailed dependencies between production tasks
2. BMissing privileges on governed data objects
3. CSlow execution of downstream analytic queries
4. DMissing or unexpected data in incoming table records

Question 3 of 10

What does the final lakehouse layer provide to end users?

1. APipeline definitions awaiting transformation execution
2. BRaw source data preserved before verification
3. CClean, enriched data designed for downstream use cases
4. DConnector configurations awaiting source ingestion

Question 4 of 10

What is the primary responsibility of Lakeflow pipelines in the end-to-end journey?

1. AIngest source data through managed and standard connectors for external systems
2. BBuild and manage batch and streaming transformations through a declarative framework
3. CRegister governed tables and track their transformation lineage across the lakehouse
4. DSchedule heterogeneous production tasks and monitor their execution as a job

Question 5 of 10

What production responsibility belongs to Lakeflow Jobs?

1. AInteractive authoring of every transformation in a SQL editor
2. BCentral governance of tables and machine learning models
3. CStorage-format enforcement for every raw source file
4. DOrchestration and monitoring of data and AI workloads

Question 6 of 10

Which set consists of targets that a Lakeflow pipeline can orchestrate?

1. ANotebooks, managed connectors, and SQL query tasks
2. BStreaming tables, materialized views, and sinks
3. CCatalogs, schemas, and governed volumes
4. DDatabases, enterprise applications, and local files

Question 7 of 10

Which sequence best represents the typical data engineering journey taught in this introduction?

1. AOrchestrate production tasks, serve results, ingest sources, and then cleanse data
2. BTransform source data before ingestion, serve it, and then configure orchestration
3. CServe raw data, transform it, ingest sources, and apply governance at the end
4. DIngest, establish governed storage, transform and refine, then serve

Question 8 of 10

A project receives batch and streaming files, must detect unexpected data as tables are created, and must preserve governed lineage during refinement. Which sequence places the responsibilities correctly?

1. AServe raw data to users, convert it to workspace files, and use compute clusters for governance
2. BRegister raw files as job tasks, convert them to notebooks, and use SQL warehouses for lineage
3. CLand raw data, convert it to Delta tables, and register the tables with Unity Catalog
4. DTransform data before it lands, convert it to Git folders, and use dashboards for schema enforcement

Question 9 of 10

A team needs a declarative SQL or Python solution for both batch and streaming transformations, and it wants automatic coordination of flows and data targets. Which resource fits?

1. AA Databricks account
2. BA Unity Catalog metastore
3. CA Lakeflow managed connector
4. DA declarative Lakeflow pipeline

Question 10 of 10

A production process must run a notebook, a managed connector, and a SQL query as coordinated tasks, while operators monitor execution. Which resource should contain the tasks?

1. AA Lakeflow job
2. BA streaming table
3. CA SQL warehouse only
4. DA Unity Catalog volume