Exam Name: Databricks Certified Professional Data Engineer Exam
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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- Total Questions: 217
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Databricks Databricks-Certified-Professional-Data-Engineer Exam Overview:
| Certification Vendor: | Databricks |
|---|---|
| Exam Name: | Databricks Certified Professional Data Engineer Exam |
| Exam Number: | Databricks-Certified-Professional-Data-Engineer |
| Exam Format: | Multiple choice, Multiple select, Scenario-based questions |
| Related Certifications: | Databricks Certified Data Engineer Associate |
| Available Languages: | English |
| Passing Score: | 70% |
| Real Exam Qty: | 60-70 |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 120 minutes |
| Exam Price: | USD 200 |
| Recommended Training: | Databricks Academy |
| Exam Registration: | Databricks Certification Portal |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored exam |
| Pre Condition: | Recommended: Databricks Certified Data Engineer Associate or equivalent experience with Spark and Databricks platform. |
| Official Syllabus URL: | https://www.databricks.com/learn/certification |
Databricks Databricks-Certified-Professional-Data-Engineer Exam Syllabus Topics:
| Section | Objectives |
|---|---|
| Production Pipelines and Orchestration | - Automate ETL pipelines and scheduling - Pipeline reliability and fault tolerance - Build and manage workflows using Databricks Jobs |
| Security, Governance, Monitoring, and Optimization | - Cost optimization and performance tuning - Implement Unity Catalog governance and access control - Monitor and optimize Spark workloads |
| Data Modeling and Storage | - Schema evolution and data partitioning strategies - Design scalable data lakehouse architectures - Delta Lake table design and optimization |
| Data Ingestion and Transformation | - Handle batch and streaming data pipelines - Transform and clean datasets using Spark SQL and DataFrame APIs - Ingest data using Apache Spark and Databricks |
Databricks-Certified-Professional-Data-Engineer Practice Questions: Answers
The Databricks Certified Professional Data Engineer exam requires a score of 70% to pass, and the official registration fee is USD 200. Knowing both numbers before you register keeps your preparation budget realistic.
The Databricks Certified Professional Data Engineer practice questions are organized around the official exam objectives. Key topics include:
- Data Ingestion and Transformation ()
- Data Modeling and Storage ()
- Production Pipelines and Orchestration ()
A systematic pass through these topics covers the syllabus end to end.
You can register for the Databricks Certified Professional Data Engineer exam through the official channels:
Recommended: Databricks Certified Data Engineer Associate or equivalent experience with Spark and Databricks platform.
You can confirm the current eligibility requirements on the official Databricks-Certified-Professional-Data-Engineer exam page before you register.
Our experts review the question bank every day, and whenever a new version is released, our system sends it to your email automatically — no request needed. If an update ever seems to go missing, check your junk or spam folder first, since automated messages occasionally land there; contact us if it truly never arrived.
Many candidates combine practice questions with official training. Recommended options include:
The Databricks Certified Professional Data Engineer exam contains 60-70 questions, and you will have 120 minutes minutes to complete them. Practicing under these exact constraints makes the real exam feel like one more rehearsal.
Yes. If anything about the updated material or your preparation is unclear, you can contact our experts directly after purchase. They will work through your question and offer professional advice on how to approach the material, so the product comes with guidance, not just files.
This package includes 217 practice questions for the Databricks Certified Professional Data Engineer exam. Every answer is verified by an experienced team of experts, so you can trust what you study.
Databricks Certified Professional Data Engineer Sample Questions:
Given the following PySpark code snippet in a Databricks notebook:
filtered_df = spark.read.format( " delta " ).load( " /mnt/data/large_table " ) \
.filter( " event_date > ' 2024-01-01 ' " )
filtered_df.count()
The data engineer notices from the Query Profiler that the scan operator for filtered_df is reading almost all files, despite the filter being applied.
What is the probable reason for poor data skipping?
- A. The event_date column is outside the table's partitioning and Z-ordering scheme.
- B. The filter condition involves a data type excluded from data skipping support.
- C. The Delta table lacks optimization that enables dynamic file pruning.
- D. The filter is executed only after the full data scan, preventing data skipping.
Correct Answer: A 🗳️
Explanation: Only visible for TestKingIT members. You can sign-up / login (it's free).
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?
- A. libraries
- B. jobs
- C. configure
- D. fs
- E. workspace
Correct Answer: A 🗳️
Explanation: Only visible for TestKingIT members. You can sign-up / login (it's free).
Which approach demonstrates a modular and testable way to use DataFrame transform for ETL code in PySpark?
- A. class Pipeline:
def transform(self, df):
return df.withColumn( " value_upper " , upper(col( " value " )))
pipeline = Pipeline()
assertDataFrameEqual(pipeline.transform(test_input), expected) - B. def upper_transform(df):
return df.withColumn( " value_upper " , upper(col( " value " )))
actual = test_input.transform(upper_transform)
assertDataFrameEqual(actual, expected) - C. def transform_data(input_df):
# transformation logic here
return output_df
test_input = spark.createDataFrame([(1, " a " )], [ " id " , " value " ]) assertDataFrameEqual(transform_data(test_input), expected) - D. def upper_value(df):
return df.withColumn( " value_upper " , upper(col( " value " )))
def filter_positive(df):
return df.filter(df[ " id " ] > 0)
pipeline_df = df.transform(upper_value).transform(filter_positive)
Correct Answer: D 🗳️
Explanation: Only visible for TestKingIT members. You can sign-up / login (it's free).
The data governance team is reviewing user for deleting records for compliance with GDPR. The following logic has been implemented to propagate deleted requests from the user_lookup table to the user aggregate table.
Assuming that user_id is a unique identifying key and that all users have requested deletion have been removed from the user_lookup table, which statement describes whether successfully executing the above logic guarantees that the records to be deleted from the user_aggregates table are no longer accessible and why?
- A. No: the Delta Lake DELETE command only provides ACID guarantees when combined with the MERGE INTO command
- B. No: files containing deleted records may still be accessible with time travel until a BACUM command is used to remove invalidated data files.
- C. No: the change data feed only tracks inserts and updates not deleted records.
- D. Yes: Delta Lake ACID guarantees provide assurance that the DELETE command successed fully and permanently purged these records.
Correct Answer: B 🗳️
Explanation: Only visible for TestKingIT members. You can sign-up / login (it's free).
Given the following error traceback:
AnalysisException: cannot resolve ' heartrateheartrateheartrate ' given input columns:
[spark_catalog.database.table.device_id, spark_catalog.database.table.heartrate, spark_catalog.database.table.mrn, spark_catalog.database.table.time] The code snippet was:
display(df.select(3* " heartrate " ))
Which statement describes the error being raised?
- A. There is a type error because a DataFrame object cannot be multiplied.
- B. There is no column in the table named heartrateheartrateheartrate.
- C. There is a type error because a column object cannot be multiplied.
- D. There is a syntax error because the heartrate column is not correctly identified as a column.
Correct Answer: B 🗳️
Explanation: Only visible for TestKingIT members. You can sign-up / login (it's free).
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