Databricks Databricks-Certified-Data-Engineer-Professional : Databricks Certified Data Engineer Professional Exam

  • Exam Code: Databricks-Certified-Data-Engineer-Professional
  • Exam Name: Databricks Certified Data Engineer Professional Exam
  • Updated: Jul 01, 2026
  • Q & A: 250 Questions and Answers

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Databricks Certified Data Engineer Professional Sample Questions:

1. The data engineer is using Spark's MEMORY_ONLY storage level. Which indicators should the data engineer look for in the spark UI's Storage tab to signal that a cached table is not performing optimally?

A) Size on Disk is < Size in Memory
B) The RDD Block Name included the '' annotation signaling failure to cache
C) On Heap Memory Usage is within 75% of off Heap Memory usage
D) Size on Disk is> 0
E) The number of Cached Partitions> the number of Spark Partitions


2. An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A) Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
B) Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
C) Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
D) Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.
E) Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.


3. A data engineer is masking a column containing email addresses. The goal is to produce output strings of identical length for all rows, while generating different outputs for different email values.
Which SQL function should be used to achieve this?

A) sha2(email, 0)
B) mask(email, '?')
C) sha1(email)
D) hash(email)


4. A data engineer is building a Lakeflow Declarative Pipelines pipeline to process healthcare claims data. A metadata JSON file defines data quality rules for multiple tables, including:
{
"claims": [
{"name": "valid_patient_id", "constraint": "patient_id IS NOT NULL"},
{"name": "non_negative_amount", "constraint": "claim_amount >= 0"}
]
}
The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.
How should the data engineer achieve this?

A) Reference each expectation with @dlt.expect decorators in the table declaration.
B) Invoke an external API to validate records against the metadata rules.
C) Load the JSON metadata, loop through its entries, and apply expectations using dlt.expect_all.
D) Use a SQL CONSTRAINT block referencing the JSON file path.


5. A data team is implementing an append-only Delta Lake pipeline that processes both batch and streaming data. They want to ensure that schema changes in the source data are automatically incorporated without breaking the pipeline. Which configuration should the team use when writing data to the Delta table?

A) validateSchema = false
B) mergeSchema = true
C) ignoreChanges = false
D) overwriteSchema = true


Solutions:

Question # 1
Answer: D
Question # 2
Answer: A
Question # 3
Answer: D
Question # 4
Answer: C
Question # 5
Answer: B

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