Intermediate~3h
dbt Deep-Dive in Data Engineering CoreInterview Readiness Check
The transformation framework most modern warehouses use: mental model, materializations (view/table/incremental/ephemeral), incremental strategies, ref-driven DAG, sources and freshness, tests, snapshots (SCD Type 2), Jinja macros, project structure, packages, dbt Cloud vs Core vs Fusion, SQLMesh comparison, and common anti-patterns..
dbtdbt coredbt clouddbt fusionSQLMeshmaterializationincrementalrefsource freshnessdbt testssnapshotSCDJinjamedallion architecturejaffle_shop
Learning path
Try a sample question
Your dbt project has a model that aggregates 500GB of raw event data into a daily summary. The aggregation takes 45 minutes to run. How should you materialise it, and why?
A.Materialise as 'ephemeral': dbt inlines the SQL as a CTE in every downstream model — avoids persisting data and speeds up queries
B.Materialise as 'view': dbt creates a SQL view, so downstream queries run the 500GB aggregation every time the view is queried — simpler to manage
C.Materialise as 'table': dbt drops and recreates the physical table on each run, so downstream models always query a fully computed, persisted result — appropriate when the model is expensive to compute and is queried frequently by multiple downstream models or BI tools
D.Materialise as 'incremental': always better than 'table' for large datasets regardless of the aggregation logic
Sign up free to take the full check and see the answer with an explanation.
What this module tests
- Conceptual understanding of dbt deep-dive fundamentals
- Trade-offs and design decisions in real system scenarios
- dbt and dbt core — applied knowledge
- Interview-style reasoning and explanation quality
Free to start
Take the dbt Deep-Dive check →
10 questions · Dimension scoring · AI coach feedback · Shareable result
Test your knowledge — free →No credit card · Takes 10–15 min
Frequently asked questions
- What does the dbt Deep-Dive check test?
- The check covers the transformation framework most modern warehouses use: mental model, materializations (view/table/incremental/ephemeral), incremental strategies, ref-driven dag, sources and freshness, tests, snapshots (scd type 2), jinja macros, project structure, packages, dbt cloud vs core vs fusion, sqlmesh comparison, and common anti-patterns.. Questions span beginner to advanced difficulty and test both conceptual understanding and practical trade-off reasoning.
- What keywords should I know for dbt Deep-Dive?
- Key concepts include: dbt, dbt core, dbt cloud, dbt fusion, SQLMesh, materialization, incremental, ref, source freshness, dbt tests, snapshot, SCD, Jinja, medallion architecture, jaffle_shop. These frequently appear in data engineering core interviews at product and infrastructure companies.
- How many questions are in the check?
- Each check has 10 questions — a mix of MCQ and short-answer. You get scored by dimension (e.g., scalability, consistency) so you know exactly where to focus.
- How long does it take to study dbt Deep-Dive?
- Most engineers spend around 3 hours on this module. The check itself takes about 10–15 minutes. You can retake it up to 3 times per day.
- What prerequisite knowledge do I need before dbt Deep-Dive?
- dbt Deep-Dive builds on Data Warehousing and Batch ETL & Pipeline Design. Complete those first for the best results.
- Is dbt, dbt core, dbt cloud covered?
- Yes. The dbt Deep-Dive module covers dbt, dbt core, dbt cloud among other key concepts. The check includes questions that reflect real data engineering core interview patterns.