DE-PRO Mock Exams & Practice Exam Questions | Databricks Certified Data Engineer Professional

Tip: DE‑PRO questions reward production judgment. Drill streaming correctness + incremental pipelines heavily, then move to 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 1–2 weeks): 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: production pickers → open
• Study Plan: 30/60/90 day schedules → read

Exam snapshot (high level)

• Certification: Databricks Certified Data Engineer Professional (DE‑PRO)
• Audience: data engineers operating production pipelines on Databricks
• Skills level: you should be comfortable with Spark/Delta and production patterns (streaming, idempotency, observability, performance tuning)
• 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‑PRO measures (what you should be able to do)

1) Build production-grade pipelines

• Incremental ingestion (CDC), multi-hop architecture, and lineage mindset.
• Pipeline reliability: idempotency, retries, and safe backfills.

2) Streaming correctness on Databricks

• Structured Streaming basics: triggers, watermarks, late data handling.
• Checkpointing and recovery without data corruption or duplication.

3) Delta Live Tables (DLT) design and operations

• Declarative pipeline structure, expectations (data quality), and table dependencies.
• Operational visibility and safe evolution of pipelines.

4) Performance and cost trade-offs

• Shuffle/skew intuition, file layout (small files), caching, and partition strategy.
• When to scale clusters vs change code vs change data layout.

5) Observability and troubleshooting

• Using metrics/logs to diagnose pipeline failures and performance regressions.
• Designing pipelines that are easy to recover and audit.

Common traps (what candidates miss)

• Treating streaming like batch (ignoring checkpointing and state).
• Misunderstanding watermarking and late-arriving data consequences.
• Over-indexing on “faster” changes that reduce reliability (unsafe overwrites, unbounded retries).
• Confusing file layout problems with “need a bigger cluster.”

Readiness checklist

• I can explain how checkpointing enables streaming recovery and what breaks it.
• I can choose trigger + watermark strategies for late data scenarios.
• I can design an incremental pipeline that is idempotent and backfillable.
• I can diagnose skew/shuffle problems and choose safe mitigations.
• I can explain DLT expectations and how they affect pipeline outcomes.

What to read next

• Study Plan: 30/60/90 day schedules → Open
• Syllabus: objectives by topic → Open
• Cheatsheet: production pickers → Open
• Practice: drills and mixed sets → Start