Data Engineering End-to-End Workflow: 10 practice questions

Question 1 of 10

Which sequence best represents the typical lakehouse data engineering journey?

1. AIngest, govern and store, transform and refine, orchestrate, then serve
2. BServe, discard lineage, ingest, separate consumers, then transform
3. COrchestrate reports, replace storage, isolate users, then collect files
4. DTransform dashboards, remove source data, govern notebooks, then ingest

Question 2 of 10

Which Lakeflow component provides connectors for databases, enterprise applications, files, and streaming sources?

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

Question 3 of 10

What is the purpose of the final lakehouse layer?

1. AKeep incoming source files permanently unverified and isolated
2. BServe clean, enriched data designed for downstream use cases
3. CSchedule raw ingestion without producing consumable data objects
4. DReplace governed tables with notebook-local data for each user

Question 4 of 10

Which statement correctly distinguishes Lakeflow pipelines from Lakeflow Jobs?

1. APipelines registers exam candidates; Jobs delivers certification assessments
2. BPipelines manage data transformations; Jobs orchestrates production tasks and workloads
3. CPipelines governs account identities; Jobs stores non-tabular files in volumes
4. DPipelines presents BI dashboards; Jobs replaces tables with notebook outputs

Question 5 of 10

What happens when raw files become governed Delta tables in the introductory workflow?

1. AUnity Catalog executes transformations while Delta Lake assigns workspace identities
2. BLakeflow Jobs converts tables into dashboards while notebooks replace the source files
3. CSQL warehouses register users while Git folders validate unexpected data values
4. DDelta Lake can enforce schema while Unity Catalog registers tables and tracks lineage

Question 6 of 10

Which activity belongs to data processing and curation rather than initial ingestion?

1. ALanding batch or streaming source data in its raw logical layer
2. BPresenting final enriched tables to BI and machine-learning consumers
3. CCleansing, combining, and reorganizing verified data for business needs
4. DRegistering for a proctored certification through an exam platform

Question 7 of 10

Which set contains documented task types that a Lakeflow Job can coordinate?

1. ADashboards, billing accounts, cloud regions, and exam score reports
2. BCatalogs, schemas, certification forms, and test-center bookings
3. CVolumes, user passwords, training badges, and storage invoices
4. DNotebooks, pipelines, managed connectors, and SQL queries

Question 8 of 10

A project must ingest database and streaming sources, transform both with managed batch and streaming logic, and schedule a notebook after the prepared tables are ready. Which mapping fits?

1. ALakeflow Connect for ingestion, pipelines for transformations, and Jobs for task orchestration
2. BLakeflow Jobs for connectors, Unity Catalog for transformations, and volumes for scheduling
3. CLakeflow pipelines for source systems, notebooks for governance, and tables for scheduling
4. DCatalog Explorer for ingestion, dashboards for transformations, and views for orchestration

Question 9 of 10

A team needs raw landing, detection of unexpected table structure, traceability through refinement, and clean BI-ready output. Which ordered approach meets those requirements?

1. ACreate BI views first, discard source lineage, then ingest unrelated table copies
2. BServe raw files, remove metadata, copy them into notebooks, then add dashboards
3. CLand raw data, convert to Delta, register with Unity Catalog, refine, then serve
4. DSchedule empty jobs, replace Delta with folders, then expose unverified files

Question 10 of 10

A declarative data flow must maintain streaming tables and materialized views, while a production process must also run a downstream notebook and SQL query. How should responsibilities be divided?

1. AUse a volume for data targets and a schema for the broader task sequence
2. BUse a dashboard for data targets and a view for the broader task sequence
3. CUse a pipeline for data targets and a job for the broader task sequence
4. DUse a library for data targets and a catalog for the broader task sequence