Important Databricks Certified Data Engineer Associate Exam Questions
Databricks Certified Data Engineer Associate Exam
Attempt the Data Engineer Associate practice test and solve real exam-like Databricks Certified Data Engineer Associate questions to prepare efficiently and increase your chances of success. Our Databricks Certified Data Engineer Associate practice questions match the actual Databricks Certified Data Engineer Associate Exam format, helping you enhance confidence and improve performance. With our Databricks Certified Data Engineer Associate practice exam software, you can analyze your performance, identify weak areas, and work on them effectively to boost your final Data Engineer Associate exam score.
| Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Data Engineer Associate Exam |
| Registration Code: | Databricks-Certified-Data-Engineer-Associate |
| Related Certification: | Databricks Data Engineer Associate Certification |
| Exam Audience: | Data Engineers and Data Professionals, |
Total Questions
231
Last Updated
13-08-2026
Exam Duration
90 MINUTES
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GET FULL PDFQuestion: 1
A data engineer is developing a small proof of concept in a notebook. When running the entire notebook, cluster usage spikes. The data engineer wants to keep the development experience and get real-time results.
Which cluster meets these requirements?
Question: 2
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?
Question: 3
A data engineer needs Task C to run only when Task A succeeds and Task B fails.
Which dependency configuration implements this conditional logic?
Question: 4
A developer is building a data pipeline that processes records from a Bronze table into a Silver table. The Bronze table, bronze_events, contains duplicate records because of at-least-once delivery guarantees from the upstream ingestion system. The developer writes the following PySpark code:
deduped_df = df.dropDuplicates()
deduped_df.summary("count", "mean", "stddev").show()
from pyspark.sql.functions import approx_count_distinct
deduped_df.select(approx_count_distinct("user_id")).show()
After running dropDuplicates() without arguments, some rows that differ only in the event_timestamp column remain. The developer wants to deduplicate records based only on user_id and event_type, keeping one row for each unique combination of those two columns.
Which code change achieves this deduplication requirement?
Question: 5
A data engineer is cleaning a Bronze table that receives the same customer records from multiple source systems. Duplicate rows have the same customer_id and email values but different ingestion_timestamp values. The Silver table should contain only one record for each unique combination of customer_id and email.
Which PySpark operation correctly deduplicates the records based on the business keys?
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