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

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

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q104-Q109):

NEW QUESTION # 104
A company stores account transactions in a Delta Lake table. The company needs to apply frequent account-level correlations (e.g., UPDATE statements) but wants to avoid rewriting entire Parquet files for each change to reduce file churn and improve write performance. Which Delta Lake feature should they enable?

A. Enable deletion vectors on the Delta table
B. Enable change data feed on the Delta table
C. Enable automatic file compaction on writes
D. Partition the Delta table by account_id

Answer: A

Explanation:
Deletion vectors allow Delta Lake to track row-level deletes and updates without rewriting entire Parquet files. By recording changes separately from the base files, this feature significantly reduces file churn and improves write performance for workloads with frequent row-level modifications such as account-level updates.

NEW QUESTION # 105
A data engineer is using the AUTO CDC API in Lakeflow Spark Declarative Pipeline to propagate deletions from a source table (orders_source) to a target table (orders_target). The source has Change Data Feed (CDF) enabled, but some delete events arrive out of order due to upstream delays. How does the AUTO CDC API internally ensure deletions are applied correctly despite out-of-order events?

A. It uses sequence_by to order events and retains tombstones for deleted rows until older sequences are processed.
B. It manually sorts incoming events by timestamp before applying changes.
C. It runs VACUUM on the target table to purge conflicting records.
D. It ignores deletions if they arrive after updates for the same key.

Answer: A

Explanation:
AUTO CDC uses the sequence_by column to deterministically order change events for each key.
Delete operations create tombstones that are retained until all earlier sequence values have been processed, ensuring that out-of-order delete events are still applied correctly and consistently in the target table.

NEW QUESTION # 106
Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?

A. spark.sql.files.maxPartitionBytes
B. spark.sql.autoBroadcastJoinThreshold
C. spark.sql.adaptive.advisoryPartitionSizeInBytes
D. spark.sql.adaptive.coalescePartitions.minPartitionNum
E. spark.sql.files.openCostInBytes

Answer: A

Explanation:
This is the correct answer because spark.sql.files.maxPartitionBytes is a configuration parameter that directly affects the size of a spark-partition upon ingestion of data into Spark. This parameter configures the maximum number of bytes to pack into a single partition when reading files from file- based sources such as Parquet, JSON and ORC. The default value is 128 MB, which means each partition will be roughly 128 MB in size, unless there are too many small files or only one large file.

NEW QUESTION # 107
A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A. Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.
B. Only the email and itv columns will be returned; the email column will contain all null values.
C. The email and ltv columns will be returned with the values in user itv.
D. Three columns will be returned, but one column will be named "redacted" and contain only null values.
E. The email, age. and ltv columns will be returned with the values in user ltv.

Answer: A

Explanation:
The code creates a view called email_ltv that selects the email and ltv columns from a table called user_ltv, which has the following schema: email STRING, age INT, ltv INT. The code also uses the CASE WHEN expression to replace the email values with the string "REDACTED" if the user is not a member of the marketing group. The user who executes the query is not a member of the marketing group, so they will only see the email and ltv columns, and the email column will contain the string "REDACTED" in each row.

NEW QUESTION # 108
A data engineer is designing a system to process batch patient encounter data stored in an S3 bucket, creating a Delta table (patient_encounters) with columns encounter_id, patient_id, encounter_date, diagnosis_code, and treatment_cost. The table is queried frequently by patient_id and encounter_date, requiring fast performance. Fine-grained access controls must be enforced. The engineer wants to minimize maintenance and boost performance. How should the data engineer create the patient_encounters table?

A. Create an external table in Unity Catalog, specifying an S3 location for the data files. Enable predictive optimization through table properties, and configure Unity Catalog permissions for access controls.
B. Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, schedule jobs to run OPTIMIZE and VACUUM commands daily to achieve best performance.
C. Create a managed table in Unity Catalog. Configure Unity Catalog permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.
D. Create a managed table in Hive Metastore. Configure Hive Metastore permissions for access controls, and rely on predictive optimization to enhance query performance and simplify maintenance.

Answer: C

Explanation:
Databricks documentation specifies that Unity Catalog managed tables are the preferred choice for secure, low-maintenance Delta Lake architectures. Managed tables provide full lifecycle management, including metadata, file storage, and access control integration with Unity Catalog.
Fine-grained permissions can be enforced at the column and row level through built-in Unity Catalog governance.
Additionally, Predictive Optimization (Auto Optimize + Auto Compaction) automatically manages file sizes, metadata pruning, and layout optimization, eliminating the need for manual maintenance such as scheduling OPTIMIZE or VACUUM.
External tables (A) require manual path management, and Hive Metastore tables (D) do not support Unity Catalog access policies. Therefore, creating a managed Unity Catalog table with predictive optimization provides both the security and performance benefits needed, making B the correct solution.

NEW QUESTION # 109
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