Free Databricks-Certified-Professional-Data-Engineer: Databricks Certified Data Engineer Professional Exam Questions and Answers
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Last updated: September 19, 2026
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Question #1
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?
Please select an optionIncorrectCorrect answer: B
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto. Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section; Databricks Documentation, under “DROP DUPLICATES” section.
Was this answer correct?Question #2
A data ingestion task requires a one-TB JSON dataset to be written out to Parquet with a target part-file size of 512 MB. Because Parquet is being used instead of Delta Lake, built- in file-sizing features such as Auto-Optimize & Auto-Compaction cannot be used. Which strategy will yield the best performance without shuffling data?
Please select an optionIncorrectCorrect answer: A
The key to efficiently converting a large JSON dataset to Parquet files of a specific size without shuffling data lies in controlling the size of the output files directly. • Setting spark.sql.files.maxPartitionBytes to 512 MB configures Spark to process data in chunks of 512 MB. This setting directly influences the size of the part-files in the output, aligning with the target file size. • Narrow transformations (which do not involve shuffling data across partitions) can then be applied to this data. • Writing the data out to Parquet will result in files that are approximately the size specified by spark.sql.files.maxPartitionBytes, in this case, 512 MB. • The other options involve unnecessary shuffles or repartitions (B, C, D) or an incorrect setting for this specific requirement (E). References: • Apache Spark Documentation: Configuration - spark.sql.files.maxPartitionBytes • Databricks Documentation on Data Sources: Databricks Data Sources Guide
Was this answer correct?Question #3
A user new to Databricks is trying to troubleshoot long execution times for some pipeline logic they are working on. Presently, the user is executing code cell-by- cell, using display() calls to confirm code is producing the logically correct results as new transformations are added to an operation. To get a measure of average time to execute, the user is running each cell multiple times interactively. Which of the following adjustments will get a more accurate measure of how code is likely to perform in production?
Please select an optionIncorrectCorrect answer: E
In Databricks notebooks, using the display() function triggers an action that forces Spark to execute the code and produce a result. However, Spark operations are generally divided into transformations and actions. Transformations create a new dataset from an existing one and are lazy, meaning they are not computed immediately but added to a logical plan. Actions, like display(), trigger the execution of this logical plan. Repeatedly running the same code cell can lead to misleading performance measurements due to caching. When a dataset is used multiple times, Spark's optimization mechanism caches it in memory, making subsequent executions faster. This behavior does not accurately represent the first-time execution performance in a production environment where data might not be cached yet. To get a more realistic measure of performance, it is recommended to: • Clear the cache or restart the cluster to avoid the effects of caching. • Test the entire workflow end-to-end rather than cell-by-cell to understand the cumulative performance. • Consider using a representative sample of the production data, ensuring it includes various cases the code will encounter in production. References: • Databricks Documentation on Performance Optimization: Databricks Performance Tuning • Apache Spark Documentation: RDD Programming Guide - Understanding transformations and actions
Was this answer correct?Question #4
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure. The silver_device_recordings table will be used downstream to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications. The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields. Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?
Please select an optionIncorrectCorrect answer: D
This is the correct answer because it accurately presents information about Delta Lake and Databricks that may impact the decision-making process of a junior data engineer who is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields. Delta Lake and Databricks support schema inference and evolution, which means that they can automatically infer the schema of a table from the source data and allow adding new columns or changing column types without affecting existing queries or pipelines. However, schema inference and evolution may not always be desirable or reliable, especially when dealing with complex or nested data structures or when enforcing data quality and consistency across different systems. Therefore, setting types manually can provide greater assurance of data quality enforcement and avoid potential errors or conflicts due to incompatible or unexpected data types. Verified References: [Databricks Certified Data Engineer Professional], under “Delta Lake” section; Databricks Documentation, under “Schema inference and partition of streaming DataFrames/Datasets” section.
Was this answer correct?Question #5
A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources. Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table. Given the current implementation, which method can be used?
Please select an optionIncorrectCorrect answer: C
Delta Lake provides built-in versioning and time travel capabilities, allowing users to query previous snapshots of a table. This feature is particularly useful for understanding changes between different versions of the table. In this scenario, where the table is overwritten nightly, you can use Delta Lake's time travel feature to execute a query comparing the latest version of the table (the current state) with its previous version. This approach effectively identifies the differences (such as new, updated, or deleted records) between the two versions. The other options do not provide a straightforward or efficient way to directly compare different versions of a Delta Lake table. References: • Delta Lake Documentation on Time Travel: Delta Time Travel • Delta Lake Versioning: Delta Lake Versioning Guide
Was this answer correct?Question #6
The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible. A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team. Which statement captures best practices for this situation?
Please select an optionIncorrectCorrect answer: C
The best practice in such scenarios is to ensure that production data is handled securely and with proper access controls. By granting only read access to production data in development and testing environments, it mitigates the risk of unintended data modification. Additionally, maintaining isolated databases for different environments helps to avoid accidental impacts on production data and systems. References: • Databricks best practices for securing data: https://docs.databricks.com/security/index.html
Was this answer correct?Question #7
Which of the following technologies can be used to identify key areas of text when parsing Spark Driver log4j output?
Please select an optionIncorrectCorrect answer: A
Regex, or regular expressions, are a powerful way of matching patterns in text. They can be used to identify key areas of text when parsing Spark Driver log4j output, such as the log level, the timestamp, the thread name, the class name, the method name, and the message. Regex can be applied in various languages and frameworks, such as Scala, Python, Java, Spark SQL, and Databricks notebooks. References: • https://docs.databricks.com/notebooks/notebooks-use.html#use-regular-expressions • https://docs.databricks.com/spark/latest/spark-sql/udf-scala.html#using-regular- expressions-in-udfs • https://docs.databricks.com/spark/latest/sparkr/functions/regexp_extract.html • https://docs.databricks.com/spark/latest/sparkr/functions/regexp_replace.html
Was this answer correct?Question #8
A junior data engineer has configured a workload that posts the following JSON to the Databricks REST API endpoint 2.0/jobs/create. Assuming that all configurations and referenced resources are available, which statement describes the result of executing this workload three times?
Please select an optionIncorrectCorrect answer: C
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with a name, an existing cluster id, and a notebook task. However, it does not specify any schedule or trigger for the job execution. Therefore, three new jobs with the same name and configuration will be created in the workspace, but none of them will be executed until they are manually triggered or scheduled. Verified References: [Databricks Certified Data Engineer Professional], under “Monitoring & Logging” section; [Databricks Documentation], under “Jobs API - Create” section.
Was this answer correct?Question #9
A data engineer is configuring a pipeline that will potentially see late-arriving, duplicate records. In addition to de-duplicating records within the batch, which of the following approaches allows the data engineer to deduplicate data against previously processed records as it is inserted into a Delta table?
Please select an optionIncorrectCorrect answer: C
To deduplicate data against previously processed records as it is inserted into a Delta table, you can use the merge operation with an insert-only clause. This allows you to insert new records that do not match any existing records based on a unique key, while ignoring duplicate records that match existing records. For example, you can use the following syntax: MERGE INTO target_table USING source_table ON target_table.unique_key = source_table.unique_key WHEN NOT MATCHED THEN INSERT * This will insert only the records from the source table that have a unique key that is not present in the target table, and skip the records that have a matching key. This way, you can avoid inserting duplicate records into the Delta table. References: • https://docs.databricks.com/delta/delta-update.html#upsert-into-a-table-using- merge • https://docs.databricks.com/delta/delta-update.html#insert-only-merge
Was this answer correct?Question #10
Which statement characterizes the general programming model used by Spark Structured Streaming?
Please select an optionIncorrectCorrect answer: D
This is the correct answer because it characterizes the general programming model used by Spark Structured Streaming, which is to treat a live data stream as a table that is being continuously appended. This leads to a new stream processing model that is very similar to a batch processing model, where users can express their streaming computation using the same Dataset/DataFrame API as they would use for static data. The Spark SQL engine will take care of running the streaming query incrementally and continuously and updating the final result as streaming data continues to arrive. Verified References: [Databricks Certified Data Engineer Professional], under “Structured Streaming” section; Databricks Documentation, under “Overview” section.
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Other Databricks certifications
- Certified Machine Learning Associate (opens in a new tab)
- Certified Machine Learning Professional (opens in a new tab)
- Certified Data Engineer Associate (opens in a new tab)
- Certified Data Analyst Associate (opens in a new tab)
- Certified Associate Developer for Apache Spark (opens in a new tab)
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FAQ
Learn More: https://www.databricks.com/learn/training/certification
- Q1: What are Databricks Certification Exams?
- A: Databricks Certification Exams validate your expertise in using Databricks’ unified data analytics platform. These certifications demonstrate your proficiency in data engineering, data analysis, and machine learning, utilizing Databricks tools and the Apache Spark framework.
- Q2: Why should I pursue Databricks Certification?
- A: Databricks Certification enhances your professional credibility, showcasing your skills and knowledge in big data analytics and machine learning. This can lead to better job opportunities, higher salaries, and career advancement in data science, data engineering, and IT industries.
- Q3: What are the benefits of Databricks Certification?
- A: Benefits include recognition as a certified data professional, improved job performance, access to exclusive resources, continuing education opportunities, and staying current with the latest data analytics technologies and best practices.
- Q4: Who should take Databricks Certification Exams?
- A: Data engineers, data scientists, data analysts, machine learning practitioners, and anyone involved in processing and analyzing large datasets should consider these certifications to validate their expertise and advance their careers.
- Q5: What types of Databricks Certification Exams are available?
- A: Databricks offers various certification paths, including:
- Q6: How do I prepare for Databricks Certification Exams?
- A: Preparation can include official Databricks training courses, study guides, practice exams, online tutorials, and hands-on experience with Databricks tools and the Apache Spark framework.
- Q7: Where can I take Databricks Certification Exams?
- A: Databricks Certification Exams can be taken online, providing flexibility to fit your schedule and location.
- Q8: How do Databricks Certifications impact my career?
- A: Databricks Certifications significantly boost your career by demonstrating your expertise to employers, making you a more competitive candidate for advanced roles and promotions in data science, data engineering, and IT.
- Q9: Are there any prerequisites for Databricks Certification Exams?
- A: Some exams may have prerequisites, such as foundational knowledge or prior experience with Databricks and Apache Spark. Check the specific requirements for each certification path on the Databricks website.
- Q10: How often do I need to recertify for Databricks Certifications?
- A: Databricks Certifications typically require recertification every two years to ensure that certified professionals stay updated with the latest data analytics technologies and industry practices.