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

Section	Weight	Objectives
Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Bronze, silver, gold data layers 2. Data governance fundamentals 3. Differences between data lake, data warehouse, and lakehouse
Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. Window functions 2. Spark SQL queries and functions 3. DataFrame operations and transformations - Python and SQL for data engineering 1. Spark APIs in Python 2. Built-in and user-defined functions 3. Performance optimization techniques
Data Ingestion	15-20%	- Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals - Batch ingestion methods 1. DBR autoloader 2. Spark APIs for ingestion 3. Integration with external systems
Delta Lake	20-25%	- Delta Lake operations 1. Schema evolution and enforcement 2. Delta Live Tables 3. Merge, update, delete operations - Delta Lake fundamentals 1. Time travel and data versioning 2. ACID transactions 3. Optimize and Z-order
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Monitoring and alerting 2. Task dependencies and orchestration 3. Jobs and job scheduling

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q35-Q40):

NEW QUESTION # 35
The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalid latitude and longitude values in the activity_details table have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to add CHECK constraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

A. The activity details table already exists; CHECK constraints can only be added during initial table creation.
B. The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.
C. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
D. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
E. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.

Answer: E

Explanation:
The failure is that the code to add CHECK constraints to the Delta Lake table fails when executed. The code uses ALTER TABLE ADD CONSTRAINT commands to add two CHECK constraints to a table named activity_details. The first constraint checks if the latitude value is between -90 and 90, and the second constraint checks if the longitude value is between -180 and 180. The cause of this failure is that the activity_details table already contains records that violate these constraints, meaning that they have invalid latitude or longitude values outside of these ranges. When adding CHECK constraints to an existing table, Delta Lake verifies that all existing data satisfies the constraints before adding them to the table. If any record violates the constraints, Delta Lake throws an exception and aborts the operation. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Add a CHECK constraint to an existing table" section.
https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-alter-table.html#add-constraint

NEW QUESTION # 36
A CHECK constraint has been successfully added to the Delta table named activity_details using the following logic:

A batch job is attempting to insert new records to the table, including a record where latitude = 45.50 and longitude = 212.67.
Which statement describes the outcome of this batch insert?

A. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
B. The write will fail completely because of the constraint violation and no records will be inserted into the target table.
C. The write will fail when the violating record is reached; any records previously processed will be recorded to the target table.
D. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.
E. The write will insert all records except those that violate the table constraints; the violating records will be reported in a warning log.

Answer: B

Explanation:
The CHECK constraint is used to ensure that the data inserted into the table meets the specified conditions. In this case, the CHECK constraint is used to ensure that the latitude and longitude values are within the specified range. If the data does not meet the specified conditions, the write operation will fail completely and no records will be inserted into the target table. This is because Delta Lake supports ACID transactions, which means that either all the data is written or none of it is written. Therefore, the batch insert will fail when it encounters a record that violates the constraint, and the target table will not be updated. References:
* Constraints : https://docs.delta.io/latest/delta-constraints.html
* ACID Transactions : https://docs.delta.io/latest/delta-intro.html#acid-transactions

NEW QUESTION # 37
A data engineering team is migrating off its legacy Hadoop platform. As part of the process, they are evaluating storage formats for performance comparison. The legacy platform uses ORC and RCFile formats.
After converting a subset of data to Delta Lake , they noticed significantly better query performance. Upon investigation, they discovered that queries reading from Delta tables leveraged a Shuffle Hash Join , whereas queries on legacy formats used Sort Merge Joins . The queries reading Delta Lake data also scanned less data.
Which reason could be attributed to the difference in query performance?

A. Shuffle Hash Joins are always more efficient than Sort Merge Joins.
B. The queries against the Delta Lake tables were able to leverage the dynamic file pruning optimization.
C. The queries against the ORC tables leveraged the dynamic data skipping optimization but not the dynamic file pruning optimization.
D. Delta Lake enables data skipping and file pruning using a vectorized Parquet reader.

Answer: D

Explanation:
Delta Lake outperforms legacy Hadoop formats because it leverages Parquet-based storage , data skipping
, and file pruning . According to Databricks documentation, Delta Lake automatically stores detailed statistics (min/max values and file-level metadata) in the transaction log. During query planning, the engine uses these statistics to skip entire files that do not match query filters , a process called data skipping and file pruning . Additionally, Delta uses a vectorized Parquet reader , which reduces I/O and CPU overhead.
Together, these optimizations allow Delta to scan significantly less data and produce more efficient physical query plans (e.g., Shuffle Hash Join instead of Sort Merge Join). The performance gain is due to efficient data skipping, not the inherent superiority of join type.

NEW QUESTION # 38
A data engineer is configuring a Lakeflow Declarative Pipeline to process CDC (Change Data Capture) data from a source. The source events sometimes arrive out of order, and multiple updates may occur with the same update_timestamp but with different update_sequence_id .
What should the data engineer do to ensure events are sequenced correctly?

A. O Use SEQUENCE BY STRUCT(event_timestamp, update_sequence_id) in AUTO CDC APIs.
B. O Use dropDuplicates() to remove out-of-order and duplicate records in LDP.
C. O Use a window function to sort update_sequence_id within the same partition, i.e., update_timestamp in the LDP pipeline.
D. O Set track_history_column_list to [event_timestamp, event_id] in AUTO CDC APIs.

Answer: A

Explanation:
When handling CDC data , sequencing is critical because updates may arrive out of order or multiple changes may occur for the same record at the same timestamp. Databricks' AUTO CDC APIs provide built- in constructs to handle ordering logic.
The correct mechanism is to use the SEQUENCE BY clause in the CDC configuration. Specifically, when both update_timestamp and update_sequence_id exist, the recommended approach is:
SEQUENCE BY STRUCT(event_timestamp, update_sequence_id)
This ensures that within the same record key, the engine applies updates in the exact sequence they occurred, resolving conflicts where multiple updates share the same timestamp but differ in sequence ID.
* Option A (track_history_column_list) is used for historical tracking and auditing changes, not for sequencing logic. It ensures lineage but does not enforce correct event order.
* Option B (dropDuplicates()) only removes exact duplicates; it cannot guarantee sequencing correctness when multiple updates exist.
* Option C is correct: SEQUENCE BY STRUCT(event_timestamp, update_sequence_id) explicitly enforces ordering, as recommended by the CDC pipeline guidelines.
* Option D (window function) would be a manual approach in Spark Structured Streaming, but Lakeflow Declarative Pipelines already provide native CDC sequencing support , making this unnecessary.
Thus, the best practice per Databricks CDC documentation is to use Option C with SEQUENCE BY STRUCT.
Reference: Databricks Lakeflow Declarative Pipelines - AUTO CDC APIs and Sequencing with SEQUENCE BY

NEW QUESTION # 39
Which of the following python statement can be used to replace the schema name and table name in the query statement?

A. 1.table_name = "sales"
2.schema_name = "bronze"
3.query = f"select * from + schema_name +"."+table_name"
B. 1.table_name = "sales"
2.schema_name = "bronze"
3.query = "select * from {schema_name}.{table_name}"
C. 1.table_name = "sales"
2.schema_name = "bronze"
3.query = f"select * from schema_name.table_name"
D. 1.table_name = "sales"
2.schema_name = "bronze"
3.query = f"select * from { schema_name}.{table_name}"

Answer: D

Explanation:
Explanation
Answer is
table_name = "sales"
query = f"select * from {schema_name}.{table_name}"
f strings can be used to format a string. f" This is string {python variable}"
https://realpython.com/python-f-strings/

NEW QUESTION # 40
......

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