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

Section	Weight	Objectives
Topic 1: Monitoring and Troubleshooting	16%	- Troubleshooting - Monitoring - Performance Optimization
Topic 2: Data Quality and Governance	12%	- Governance - Data Lineage - Data Quality
Topic 3: Data Modeling and Storage	20%	- File Formats - Data Modeling - Storage Optimization
Topic 4: Databricks Lakehouse Platform	24%	- Data Management - Unity Catalog - Delta Lake - Lakehouse Architecture
Topic 5: Data Processing	28%	- Structured Streaming - Data Transformation - ETL Pipelines - Spark SQL

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q225-Q230):

NEW QUESTION # 225
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A. - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor
B. - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
C. - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor
D. - Total VMs: 16
- 25 GB per Executor
- 10 Cores / Executor
E. - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor

Answer: D

NEW QUESTION # 226
A data engineer has a Delta table orders with deletion vectors enabled. The engineer executes the following command:
DELETE FROM orders WHERE status = 'cancelled';
What should be the behavior of deletion vectors when the command is executed?

A. Rows are marked as deleted in metadata, not in files.
B. Delta automatically removes all cancelled orders permanently.
C. Files are physically rewritten without the deleted rows.
D. Rows are marked as deleted both in metadata and in files.

Answer: A

Explanation:
Deletion vectors (DVs) in Delta Lake optimize delete operations by marking deleted rows logically in metadata rather than rewriting Parquet files. When a DELETE statement is executed, affected rows are tracked by DVs in the transaction log. The data remains in the underlying files but is filtered out during query reads. This improves performance for frequent deletes and updates since file rewrites are deferred. Physical data removal only occurs when a VACUUM command is later executed. The Databricks documentation confirms: "With deletion vectors, deleted rows are marked in metadata and skipped at read time, avoiding file rewrites." Thus, rows are marked as deleted in metadata--not in files.

NEW QUESTION # 227
A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?

A. Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
"cloudFiles.schemaEvolutionMode"= "addNewColumns"
B. Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
C. Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")
D. Use Auto Loader in batch mode with a daily job to overwrite the Delta table.

Answer: A

Explanation:
Lakeflow Spark Declarative Pipelines combined with Auto Loader provide fully managed incremental file ingestion with exactly-once guarantees and minimal operational overhead.
Enabling schema inference and evolution allows new columns in incoming JSON files to be incorporated automatically, satisfying the requirements for streaming ingestion, schema evolution, and reduced manual infrastructure management.

NEW QUESTION # 228
How are the operational aspects of Lakeflow Declarative Pipelines different from Spark Structured Streaming?

A. Structured Streaming can process continuous data streams, while Lakeflow Declarative Pipelines cannot.
B. Lakeflow Declarative Pipelines automatically handle schema evolution, while Structured Streaming always requires manual schema management.
C. Lakeflow Declarative Pipelines manage the orchestration of multi-stage pipelines automatically, while Structured Streaming requires external orchestration for complex dependencies.
D. Lakeflow Declarative Pipelines can write to Delta Lake format, while Structured Streaming cannot.

Answer: C

Explanation:
Databricks documentation explains that Lakeflow Declarative Pipelines build upon Structured Streaming but add higher-level orchestration and automation capabilities. They automatically manage dependencies, materialization, and recovery across multi-stage data flows without requiring external orchestration tools such as Airflow or Azure Data Factory. In contrast, Structured Streaming operates at a lower level, where developers must manually handle orchestration, retries, and dependencies between streaming jobs. Both support Delta Lake outputs and schema evolution; however, Lakeflow Declarative Pipelines simplify management by declaratively defining transformations and data quality expectations. Hence, the correct distinction is A -- automated orchestration and management in Lakeflow Declarative Pipelines.

NEW QUESTION # 229
A table is registered with the following code:
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Both users and orders are Delta Lake tables. Which statement describes the results of querying recent_orders?

A. The versions of each source table will be stored in the table transaction log; query results will be saved to DBFS with each query.
B. Results will be computed and cached when the table is defined; these cached results will incrementally update as new records are inserted into source tables.
C. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query began.
D. All logic will execute at query time and return the result of joining the valid versions of the source tables at the time the query finishes.
E. All logic will execute when the table is defined and store the result of joining tables to the DBFS; this stored data will be returned when the table is queried.

Answer: E

Explanation:
Table is created and data of join will be stored on DBFS and it will be returned on query time.

NEW QUESTION # 230
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