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

Section	Objectives
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	- Delta Lake table design and optimization - Schema evolution and data partitioning strategies - Design scalable data lakehouse architectures
Production Pipelines and Orchestration	- Pipeline reliability and fault tolerance - Build and manage workflows using Databricks Jobs - Automate ETL pipelines and scheduling
Data Ingestion and Transformation	- Transform and clean datasets using Spark SQL and DataFrame APIs - Ingest data using Apache Spark and Databricks - Handle batch and streaming data pipelines

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q82-Q87):

NEW QUESTION # 82
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. Ingest the data, execute the narrow transformations, repartition to 2,048 partitions (1TB* 1024*1024
/512), 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. 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.
D. Set spark.sql.files.maxPartitionBytes to 512 MB, ingest the data, execute the narrow transformations, and then write to parquet.
E. Set spark.sql.shuffle.partitions to 512, ingest the data, execute the narrow transformations, and then write to parquet.

Answer: D

Explanation:
For this scenario where a one-TB JSON dataset needs to be converted into Parquet format without employing Delta Lake's auto-sizing features, the goal is to avoid unnecessary data shuffles and yet ensure optimal file sizes for the output Parquet files. Here's a breakdown of why option A is most suitable:
* Setting maxPartitionBytes:The spark.sql.files.maxPartitionBytes configuration controls the size of blocks that Spark reads from the data source (in this case, the JSON files) but also influences the output size of files when data is written without repartition or coalesce operations. Setting this parameter to
512 MB directly addresses the requirement to manage the output file size effectively.
* Data Ingestion and Processing:
* Ingesting Data:Load the JSON dataset into a DataFrame.
* Applying Transformations:Perform any required narrow transformations that do not involve shuffling data (like filtering or adding new columns).
* Writing to Parquet:Directly write the transformed DataFrame to Parquet files. The setting for maxPartitionBytes ensures that each part-file is approximately 512 MB, meeting the requirement for part-file size without additional steps to repartition or coalesce the data.
* Performance Consideration:This approach is optimal because:
* It avoids the overhead of shuffling data, which can be significant, especially with large datasets.
* It directly ties the read/write operations to a configuration that matches the target output size, making it efficient in terms of both computation and I/O operations.
* Alternative Options Analysis:
* Option B and D:Involves repartitioning, which would trigger a shuffle of the data, contradicting the requirement to avoid shuffling for performance reasons.
* Option C:Uses coalesce, which is less intensive than repartition but can still lead to uneven partition sizes and does not directly control the output file size as effectively as setting maxPartitionBytes.
* Option E:Setting shuffle partitions to 512 doesn't directly control the output file size for writing to Parquet and could lead to smaller files depending on the dataset's partitioning post- transformations.
References
* Apache Spark Configuration
* Writing to Parquet Files in Spark

NEW QUESTION # 83
A data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs. A DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens.
Which statement describes the contents of the workspace audit logs concerning these events?

A. Because User B last configured the jobs, their identity will be associated with both the job creation events and the job run events.
B. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events.
C. Because the REST API was used for job creation and triggering runs, user identity will not be captured in the audit logs.
D. Because the REST API was used for job creation and triggering runs, a Service Principal will be automatically used to identity these events.
E. Because User A created the jobs, their identity will be associated with both the job creation events and the job run events.

Answer: B

Explanation:
Explanation
The events are that a data engineer, User A, has promoted a new pipeline to production by using the REST API to programmatically create several jobs, and a DevOps engineer, User B, has configured an external orchestration tool to trigger job runs through the REST API. Both users authorized the REST API calls using their personal access tokens. The workspace audit logs are logs that record user activities in a Databricks workspace, such as creating, updating, or deleting objects like clusters, jobs, notebooks, or tables. The workspace audit logs also capture the identity of the user who performed each activity, as well as the time and details of the activity. Because these events are managed separately, User A will have their identity associated with the job creation events and User B will have their identity associated with the job run events in the workspace audit logs. Verified References: [Databricks Certified Data Engineer Professional], under
"Databricks Workspace" section; Databricks Documentation, under "Workspace audit logs" section.

NEW QUESTION # 84
A data engineer is overwriting data in a table by deleting the table and recreating the table. Another data
engineer suggests that this is inefficient and the table should simply be overwritten instead.
Which of the following reasons to overwrite the table instead of deleting and recreating the table is incorrect?

A. Overwriting a table is efficient because no files need to be deleted
B. Overwriting a table is an atomic operation and will not leave the table in an unfinished state
C. Overwriting a table results in a clean table history for logging and audit purposes
D. Overwriting a table allows for concurrent queries to be completed while in progress
E. Overwriting a table maintains the old version of the table for Time Travel

Answer: C

NEW QUESTION # 85
The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.

Which statement describes this implementation?

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

Answer: C

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 Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Merge Into (Delta Lake on Databricks)" section.

NEW QUESTION # 86
A data engineer wants to ingest a large collection of image files (JPEG and PNG) from cloud object storage into a Unity Catalog-managed table for analysis and visualization.
Which two configurations and practices are recommended to incrementally ingest these images into the table? (Choose 2 answers)

A. Use Auto Loader and set cloudFiles.format to "TEXT".
B. Use the pathGlobFilter option to select only image files (e.g., "*.jpg,*.png").
C. Use Auto Loader and set cloudFiles.format to "BINARYFILE".
D. Move files to a volume and read with SQL editor.
E. Use Auto Loader and set cloudFiles.format to "IMAGE".

Answer: B,C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks Auto Loader supports ingestion of binary file formats using the cloudFiles.format option. For ingesting JPEG or PNG image files, the correct setting is "BINARYFILE", which loads the raw binary content and file metadata into a DataFrame. Additionally, when processing files from object storage, it is best practice to apply pathGlobFilter to limit ingestion to specific file types and reduce unnecessary scanning of non-image files. Options like "IMAGE" or "TEXT" are invalid, and using volumes with SQL editors does not provide incremental ingestion. Therefore, combining Auto Loader with cloudFiles.format="BINARYFILE" and pathGlobFilter ensures scalable, incremental ingestion of image data into Unity Catalog tables.

NEW QUESTION # 87
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