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

Section	Objectives
Topic 1: Delta Lake and Data Management	- Delta Lake transactions and ACID properties - Schema evolution and enforcement - Time travel and versioning
Topic 2: Production Pipelines and Orchestration	- Databricks Workflows - Error handling and recovery strategies - Job scheduling and monitoring
Topic 3: Data Modeling and Transformation	- Spark SQL transformations - Dimensional modeling concepts - Performance optimization techniques
Topic 4: Data Ingestion and Processing	- Structured Streaming fundamentals - ETL pipeline design patterns - Batch and streaming ingestion with Auto Loader
Topic 5: Databricks Lakehouse Platform Architecture	- Workspace and cluster architecture - Medallion architecture (Bronze, Silver, Gold) - Data governance concepts (Unity Catalog basics)

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q108-Q113):

NEW QUESTION # 108
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

A. Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
B. Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
C. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
D. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
E. The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.

Answer: D

Explanation:
This is the correct answer because it describes how Delta Lake can help to avoid data loss of this nature in the future. By ingesting all raw data and metadata from Kafka to a bronze Delta table, Delta Lake creates a permanent, replayable history of the data state that can be used for recovery or reprocessing in case of errors or omissions in downstream applications or pipelines.
Delta Lake also supports schema evolution, which allows adding new columns to existing tables without affecting existing queries or pipelines. Therefore, if a critical field was omitted from an application that writes its Kafka source to Delta Lake, it can be easily added later and the data can be reprocessed from the bronze table without losing any information.

NEW QUESTION # 109
A junior data engineer is migrating a workload from a relational database system to the Databricks Lakehouse. The source system uses a star schema, leveraging foreign key constrains and multi-table inserts to validate records on write.
Which consideration will impact the decisions made by the engineer while migrating this workload?

A. Foreign keys must reference a primary key field; multi-table inserts must leverage Delta Lake's upsert functionality.
B. Databricks supports Spark SQL and JDBC; all logic can be directly migrated from the source system without refactoring.
C. All Delta Lake transactions are ACID compliance against a single table, and Databricks does not enforce foreign key constraints.
D. Databricks only allows foreign key constraints on hashed identifiers, which avoid collisions in highly-parallel writes.
E. Committing to multiple tables simultaneously requires taking out multiple table locks and can lead to a state of deadlock.

Answer: C

Explanation:
In Databricks and Delta Lake, transactions are indeed ACID-compliant, but this compliance is limited to single table transactions. Delta Lake does not inherently enforce foreign key constraints, which are a staple in relational database systems for maintaining referential integrity between tables. This means that when migrating workloads from a relational database system to Databricks Lakehouse, engineers need to reconsider how to maintain data integrity and relationships that were previously enforced by foreign key constraints. Unlike traditional relational databases where foreign key constraints help in maintaining the consistency across tables, in Databricks Lakehouse, the data engineer has to manage data consistency and integrity at the application level or through careful design of ETL processes.

NEW QUESTION # 110
A data engineer and a platform engineer are working together to automate their system tasks. A script needs to be executed outside of Databricks only if a particular daily Databricks job finishes successfully for the day. Databricks CLI command was used to check the last execution of the job. What are the required command options for that task?

A. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS --active- only
B. databricks jobs list-runs --job-id JOB_ID --start-time-to TODAY_MIDNIGHT_EPOCH_MS -- completed-only
C. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- completed-only
D. databricks jobs list-runs --job-id JOB_ID --start-time-from TODAY_MIDNIGHT_EPOCH_MS -- active-only

Answer: C

Explanation:
Using --start-time-from with today's midnight epoch ensures only runs that started during the current day are considered, and --completed-only filters out in-progress runs. This allows the script to reliably check whether the daily job has finished successfully before triggering downstream external actions.

NEW QUESTION # 111
A new data engineer notices that a critical field was omitted from an application that writes its Kafka source to Delta Lake. This happened even though the critical field was in the Kafka source.
That field was further missing from data written to dependent, long-term storage. The retention threshold on the Kafka service is seven days. The pipeline has been in production for three months.
Which describes how Delta Lake can help to avoid data loss of this nature in the future?

A. Delta Lake schema evolution can retroactively calculate the correct value for newly added fields, as long as the data was in the original source.
B. Delta Lake automatically checks that all fields present in the source data are included in the ingestion layer.
C. Data can never be permanently dropped or deleted from Delta Lake, so data loss is not possible under any circumstance.
D. Ingestine all raw data and metadata from Kafka to a bronze Delta table creates a permanent, replayable history of the data state.
E. The Delta log and Structured Streaming checkpoints record the full history of the Kafka producer.

Answer: D

NEW QUESTION # 112
In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone. A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.
Why are the cloned tables no longer working?

A. Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.
B. The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command
C. The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.
D. Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.
E. Tables created with SHALLOW CLONE are automatically deleted after their default retention threshold of 7 days.

Answer: B

Explanation:
In Delta Lake, a shallow clone creates a new table by copying the metadata of the source table without duplicating the data files. When the vacuum command is run on the source table, it removes old data files that are no longer needed to maintain the transactional log's integrity, potentially including files referenced by the shallow clone's metadata. If these files are purged, the shallow cloned tables will reference non-existent data files, causing them to stop working properly. This highlights the dependency of shallow clones on the source table's data files and the impact of data management operations like vacuum on these clones.

NEW QUESTION # 113
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