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

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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q203-Q208):

NEW QUESTION # 203
Which statement describes Delta Lake Auto Compaction?

A. Data is queued in a messaging bus instead of committing data directly to memory; all data is committed from the messaging bus in one batch once the job is complete.
B. Optimized writes use logical partitions instead of directory partitions; because partition boundaries are only represented in metadata, fewer small files are written.
C. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 1 GB.
D. Before a Jobs cluster terminates, optimize is executed on all tables modified during the most recent job.
E. An asynchronous job runs after the write completes to detect if files could be further compacted; if yes, an optimize job is executed toward a default of 128 MB.

Answer: E

Explanation:
Explanation
This is the correct answer because it describes the behavior of Delta Lake Auto Compaction, which is a feature that automatically optimizes the layout of Delta Lake tables by coalescing small files into larger ones. Auto Compaction runs as an asynchronous job after a write to a table has succeeded and checks if files within a partition can be further compacted. If yes, it runs an optimize job with a default target file size of 128 MB.
Auto Compaction only compacts files that have not been compacted previously. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Auto Compaction for Delta Lake on Databricks" section.
"Auto compaction occurs after a write to a table has succeeded and runs synchronously on the cluster that has performed the write. Auto compaction only compacts files that haven't been compacted previously."
https://learn.microsoft.com/en-us/azure/databricks/delta/tune-file-size

NEW QUESTION # 204
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A. Task queueing resulting from improper thread pool assignment.
B. Spill resulting from attached volume storage being too small.
C. Network latency due to some cluster nodes being in different regions from the source data
D. Skew caused by more data being assigned to a subset of spark-partitions.
E. Credential validation errors while pulling data from an external system.

Answer: D

Explanation:
Explanation
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills. Verified References:
[Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Skew" section.

NEW QUESTION # 205
Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?

A. DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
B. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
C. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
D. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
E. The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.

Answer: A

Explanation:
Explanation
DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems1. DBFS is not a physical file system, but a layer over the object storage that provides a unified view of data across different data sources1. By default, the DBFS root is accessible to all users in the workspace, and the access to mounted data sources depends on the permissions of the storage account or container2. Mounted storage volumes do not need to have full public read and write permissions, but they do require a valid connection string or access key to be provided when mounting3. Both the DBFS root and mounted storage can be accessed when using %sh in a Databricks notebook, as long as the cluster has FUSE enabled4. The DBFS root does not store files in ephemeral block volumes attached to the driver, but in the object storage associated with the workspace1. Mounted directories will persist saved data to external storage between sessions, unless they are unmounted or deleted3. References: DBFS, Work with files on Azure Databricks, Mounting cloud object storage on Azure Databricks, Access DBFS with FUSE

NEW QUESTION # 206
Which statement describes integration testing?

A. Validates behavior of individual elements of your application
B. Requires an automated testing framework
C. Validates interactions between subsystems of your application
D. Requires manual intervention
E. Validates an application use case

Answer: C

Explanation:
Explanation
This is the correct answer because it describes integration testing. Integration testing is a type of testing that validates interactions between subsystems of your application, such as modules, components, or services.
Integration testing ensures that the subsystems work together as expected and produce the correct outputs or results. Integration testing can be done at different levels of granularity, such as component integration testing, system integration testing, or end-to-end testing. Integration testing can help detect errors or bugs that may not be found by unit testing, which only validates behavior of individual elements of your application. Verified References: [Databricks Certified Data Engineer Professional], under "Testing" section; Databricks Documentation, under "Integration testing" section.

NEW QUESTION # 207
The viewupdatesrepresents an incremental batch of all newly ingested data to be inserted or updated in the customerstable.
The following logic is used to process these records.

Which statement describes this implementation?

A. The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
B. The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
C. The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
D. The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
E. The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.

Answer: B

Explanation:
The logic uses the MERGE INTO command to merge new records from the view updates into the table customers. The MERGE INTO command takes two arguments: a target table and a source table or view. The command also specifies a condition to match records between the target and the source, and a set of actions to perform when there is a match or not. In this case,the condition is to match records by customer_id, which is the primary key of the customers table. The actions are to update the existing record in the target with the new values from the source, and set the current_flag to false to indicate that the record is no longer current; and to insert a new record in the target with the new values from the source, and set the current_flag to true to indicate that the record is current. This means that old values are maintained but marked as no longer current and new values are inserted, which is the definition of a Type 2 table. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Merge Into (Delta Lake on Databricks)" section.

NEW QUESTION # 208
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