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DE-ASSOC Mock Exams & Practice Exam Questions | Databricks Certified Data Engineer Associate

DE-ASSOC mock exams and practice exam questions for Databricks Certified Data Engineer Associate. Timed practice sets and detailed explanations in the AWS Exam Prep app (web, iOS, Android).

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Tip: Begin with 15–25 question topic drills (Spark SQL, Delta Lake, ETL patterns), then shift to mixed sets. Aim for consistent ~75–80% on mixed sets.

Suggested progression

  1. Topic drills (daily): 2× 15–25 questions focused on one topic from the Syllabus .
  2. Mixed sets (alternate days): 1× 25–35 questions combining 2–3 topics.
  3. Timed runs (final week): 2–3 mixed runs under time; review every miss and re-drill weak objectives.

What to pair with practice

  • Syllabus: objective-by-topic outline → view
  • Cheatsheet: Spark + Delta quick rules → open
  • Study Plan: 30/60/90 day schedules → read

Exam snapshot (high level)

  • Certification: Databricks Certified Data Engineer Associate (DE‑ASSOC)
  • Audience: data engineers and analytics engineers working in Spark/Delta Lakehouse environments
  • Skills level: you should be able to read/write Spark SQL and basic PySpark, and understand Delta Lake table behavior
  • Official details: registration, pricing, and delivery mode can change—use Resources for current info.

Study funnel: Follow the Study Plan → work the Syllabus objective-by-objective → use the Cheatsheet for recall → validate with Practice .

What DE‑ASSOC measures (what you should be able to do)

1) Use Spark effectively for ETL

  • DataFrames and Spark SQL fundamentals (joins, aggregations, windows).
  • Transformations vs actions, caching/persistence, and avoiding common anti-patterns.

2) Use Delta Lake correctly

  • ACID tables, schema enforcement/evolution, MERGE, and time travel.
  • Partitioning/file layout basics and when OPTIMIZE helps (conceptual).

3) Build reliable batch pipelines

  • Ingestion patterns (append, overwrite, incremental loads).
  • Data quality checks, idempotency mindset, and safe overwrite semantics.

4) Basic platform awareness

  • Notebooks vs Jobs/Workflows, parameters, and simple orchestration intent.
  • Reading/writing to managed tables, external tables, and common formats.

Common traps (what candidates miss)

  • Confusing transformations with actions (what triggers execution).
  • Assuming ordering guarantees that Spark/Delta doesn’t provide without explicit logic.
  • Misusing overwrite modes (clobbering data) or failing to make ETL idempotent.
  • Not understanding Delta schema rules (when writes fail vs evolve).

Readiness checklist

  • I can write joins/aggregations/windows in Spark SQL and explain the result shape.
  • I can read/write Delta tables and predict schema enforcement outcomes.
  • I can explain MERGE use cases (upsert/CDC) and common pitfalls.
  • I know basic partitioning trade-offs and when to avoid over-partitioning.
  • I can choose a safe batch ETL strategy (append vs overwrite vs incremental).
  • Study Plan: 30/60/90 day schedules → Open
  • Syllabus: objectives by topic → Open
  • Cheatsheet: high-yield Spark + Delta → Open
  • Practice: drills and mixed sets → Start