How to think through exam-style questions
Sample questions are useful only if you understand why each answer is correct. Memorizing answers helps for one question. Understanding the reasoning helps for all of them.
Below are a few exam-style questions in the spirit of the Databricks Certified Data Engineer Associate exam. These are practice examples written by BricksNotes, not real exam questions. Read the explanation slowly, then try to explain it back in your own words.
You accidentally overwrote a Delta table an hour ago. You need the previous version back. What is the simplest way to read the old data?
A. Restore from a separate backup system B. Use Delta Lake time travel to read an earlier version C. Rebuild the table from source files D. There is no way to recover it
The answer is B. Delta Lake keeps a transaction log, so you can read an earlier version by version number or timestamp. This is exactly why the Delta Lake tutorial spends time on time travel. Backups and rebuilds might work, but they are not the simplest option here.
You try to write a DataFrame that has an extra column into an existing Delta table, and the write fails. What most likely happened?
A. The cluster ran out of memory B. Schema enforcement rejected the mismatched data C. Delta Lake does not support writes D. The table was locked by another user
The answer is B. By default, Delta Lake enforces the table schema and rejects data that does not match. If you actually wanted the new column, you would enable schema evolution on purpose. Review the schema evolution lesson to see both behaviors side by side.
A teammate says the Spark SQL version of a query will run faster than the PySpark DataFrame version. Are they correct?
A. Yes, SQL is always faster B. Yes, PySpark is slower because it uses Python C. No, both compile to the same execution plan D. It depends on the cluster size only
The answer is C. PySpark and Spark SQL both go through the same Catalyst optimizer and produce the same physical plan. The syntax you choose does not change performance. If this surprises you, read PySpark vs Spark SQL.
In a medallion architecture, where should raw, unmodified source data land first?
A. Gold B. Silver C. Bronze D. Directly in a dashboard
The answer is C. Raw data lands in bronze. It gets cleaned and conformed in silver, then shaped for use in gold. The medallion architecture lesson explains why this layering keeps pipelines reliable.
You have a pipeline that reprocesses the entire dataset every night, even though only a small amount of new data arrives. What is the main problem?
A. The results will be wrong B. It wastes compute by reprocessing unchanged data C. Delta Lake cannot handle daily jobs D. Nothing, this is the recommended approach
The answer is B. Reprocessing everything works, but it is wasteful when only new data arrives. Incremental processing handles just the new records, which saves time and cost. This judgment style of question is common on the exam.
Do not just check whether you got the answer right. For each question, say out loud why the wrong options are wrong. That is the skill the exam actually tests.
When you are ready for a full attempt, take the free Associate practice exam under timed conditions. Then review every miss using the habit from our common mistakes article: write one sentence explaining the correct answer.