Free PDF Quiz 2026 Databricks-Certified-Professional-Data-Engineer: Databricks Certified Professional Data Engineer Exam Updated Test Questions Answers

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

Section	Weight	Objectives
Monitoring and Alerting	10%	- Pipeline observability and logging - Setting up alerts and notifications - Performance and health monitoring
Developing Code for Data Processing using Python and SQL	22%	- Batch and incremental processing logic - Data transformation and aggregation - Integration with Databricks APIs and tools
Data Ingestion & Acquisition	7%	- Schema inference and evolution - Connecting to diverse data sources - Auto Loader and streaming ingestion
Data Governance	7%	- Unity Catalog management - Data lineage and metadata tracking - Policy enforcement
Cost & Performance Optimisation	13%	- Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching - Cluster configuration and scaling
Data Modelling	6%	- Schema design and management - Delta Lake table design - Medallion Architecture implementation
Ensuring Data Security and Compliance	10%	- Access control and permissions - Compliance standards implementation - Data encryption and masking
Data Transformation, Cleansing, and Quality	10%	- Handling missing or inconsistent data - Data validation and quality checks - Standardization and normalization
Data Sharing and Federation	5%	- Unity Catalog data sharing - Cross-workspace and cross-cloud access
Debugging and Deploying	10%	- Troubleshooting pipelines and errors - CI/CD and DevOps practices - Deployment using bundles, CLI, and APIs

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q156-Q161):

NEW QUESTION # 156
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?

A. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.
B. Set spark.sql.shuffle.partitions to 2,048 partitions (1TB*1024*1024/512), ingest the data, execute the narrow transformations, optimize the data by sorting it (which automatically repartitions the data), and then write to parquet.
C. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB*
1024*1024/512), and then write to parquet.
D. Set spark.sql.adaptive.advisoryPartitionSizeInBytes to 512 MB bytes, ingest the data, execute the narrow transformations, coalesce to 2,048 partitions (1TB*1024*1024/512), and then write to parquet.
E. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.

Answer: B

Explanation:
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.
* Settingspark.sql.files.maxPartitionBytesto 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

NEW QUESTION # 157
The following code has been migrated to a Databricks notebook from a legacy workload:

The code executes successfully and provides the logically correct results, however, it takes over 20 minutes to extract and load around 1 GB of data.
Which statement is a possible explanation for this behavior?

A. %sh triggers a cluster restart to collect and install Git. Most of the latency is related to cluster startup time.
B. %sh executes shell code on the driver node. The code does not take advantage of the worker nodes or Databricks optimized Spark.
C. %sh does not distribute file moving operations; the final line of code should be updated to use %fs instead.
D. Python will always execute slower than Scala on Databricks. The run.py script should be refactored to Scala.
E. Instead of cloning, the code should use %sh pip install so that the Python code can get executed in parallel across all nodes in a cluster.

Answer: B

Explanation:
https://www.databricks.com/blog/2020/08/31/introducing-the-databricks-web-terminal.html The code is using %sh to execute shell code on the driver node. This means that the code is not taking advantage of the worker nodes or Databricks optimized Spark. This is why the code is taking longer to execute. A better approach would be to use Databricks libraries and APIs to read and write data from Git and DBFS, and to leverage the parallelism and performance of Spark. For example, you can use the Databricks Connect feature to run your Python code on a remote Databricks cluster, or you can use the Spark Git Connector to read data from Git repositories as Spark DataFrames.

NEW QUESTION # 158
In order to facilitate near real-time workloads, a data engineer is creating a helper function to leverage the schema detection and evolution functionality of Databricks Auto Loader. The desired function will automatically detect the schema of the source directly, incrementally process JSON files as they arrive in a source directory, and automatically evolve the schema of the table when new fields are detected.
The function is displayed below with a blank:

Which response correctly fills in the blank to meet the specified requirements?

A. Option C
B. Option E
C. Option B
D. Option A
E. Option D

Answer: C

Explanation:
Option B correctly fills in the blank to meet the specified requirements. Option B uses the "cloudFiles.
schemaLocation" option, which is required for the schema detection and evolution functionality of Databricks Auto Loader. Additionally, option B uses the "mergeSchema" option, which is required for the schema evolution functionality of Databricks Auto Loader. Finally, option B uses the "writeStream" method, which is required for the incremental processing of JSON files as they arrive in a source directory. The other options are incorrect because they either omit the required options, use the wrong method, or use the wrong format. References:
* Configure schema inference and evolution in Auto Loader : https://docs.databricks.com/en/ingestion
/auto-loader/schema.html
* Write streaming data : https://docs.databricks.com/spark/latest/structured-streaming/writing-streaming- data.html

NEW QUESTION # 159
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:
A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
B. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
C. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
D. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.
E. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.

Answer: C

Explanation:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated. References:
* Constraints: https://docs.delta.io/latest/delta-constraints.html
* ACID Transactions: https://docs.delta.io/latest/delta-intro.html#acid-transactions

NEW QUESTION # 160
You are tasked to set up a set notebook as a job for six departments and each department can run the task parallelly, the notebook takes an input parameter dept number to process the data by department, how do you go about to setup this up in job?

A. A task accepts key-value pair parameters, creates six tasks pass department number as parameter foreach task with no dependency in the job as they can all run in parallel.
(Correct)
B. Use a single notebook as task in the job and use dbutils.notebook.run to run each note-book with parameter in a different cell
C. A parameter can only be passed at the job level, create six jobs pass department number to each job with linear job dependency
D. A parameter can only be passed at the job level, create six jobs pass department number to each job with no job dependency
E. A task in the job cannot take an input parameter, create six notebooks with hardcoded dept number and setup six tasks with linear dependency in the job

Answer: A

Explanation:
Explanation
Here is how you setup
Create a single job and six tasks with the same notebook and assign a different parameter for each task , Graphical user interface, text, application, email Description automatically generated

All tasks are added in a single job and can run parallel either using single shared cluster or with individual clusters.
Graphical user interface, application, Teams Description automatically generated

NEW QUESTION # 161
......

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