Databricks-Certified-Professional-Data-Engineer Actual Questions Update in a High Speed - Exam-Killer

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

SectionWeightObjectives
Data Warehouse and Lakehouse Architecture15-20%- Lakehouse architecture principles
  • 1. Bronze, silver, gold data layers
  • 2. Differences between data lake, data warehouse, and lakehouse
  • 3. Data governance fundamentals
Data Processing with Spark25-30%- Python and SQL for data engineering
  • 1. Performance optimization techniques
  • 2. Built-in and user-defined functions
  • 3. Spark APIs in Python
- Spark DataFrames and Spark SQL
  • 1. DataFrame operations and transformations
  • 2. Spark SQL queries and functions
  • 3. Window functions
Delta Lake20-25%- Delta Lake operations
  • 1. Merge, update, delete operations
  • 2. Delta Live Tables
  • 3. Schema evolution and enforcement
- Delta Lake fundamentals
  • 1. ACID transactions
  • 2. Time travel and data versioning
  • 3. Optimize and Z-order
Data Ingestion15-20%- Batch ingestion methods
  • 1. Spark APIs for ingestion
  • 2. DBR autoloader
  • 3. Integration with external systems
- Streaming ingestion
  • 1. Kafka integration
  • 2. Structured streaming fundamentals
Pipeline Development and Orchestration10-15%- Databricks workflows
  • 1. Jobs and job scheduling
  • 2. Task dependencies and orchestration
  • 3. Monitoring and alerting

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q58-Q63):

NEW QUESTION # 58
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?

Answer: B

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 # 59
You were asked to identify number of times a temperature sensor exceed threshold temperature (100.00) by each device, each row contains 5 readings collected every 5 minutes, fill in the blank with the appropriate functions.
Schema: deviceId INT, deviceTemp ARRAY<double>, dateTimeCollected TIMESTAMP

SELECT deviceId, __ (__ (__(deviceTemp], i -> i > 100.00)))
FROM devices
GROUP BY deviceId

Answer: D

Explanation:
Explanation
FILER function can be used to filter an array based on an expression
SIZE function can be used to get size of an array
SUM is used to calculate to total by device
Diagram Description automatically generated


NEW QUESTION # 60
When you drop an external DELTA table using the SQL Command DROP TABLE table_name, how does it impact metadata(delta log, history), and data stored in the storage?

Answer: E

Explanation:
Explanation
The answer is Drops table from metastore, but keeps metadata and data in storage.
When an external table is dropped, only the table definition is dropped from metastore everything including data and metadata(Delta transaction log, time travel history) remains in the storage. Delta log is considered as part of metadata because if you drop a column in a delta table(managed or external) the column is not physically removed from the parquet files rather it is recorded in the delta log. The delta log becomes a key metadata layer for a Delta table to work.
Please see the below image to compare the external delta table and managed delta table and how they differ in how they are created and what happens if you drop the table.
Diagram Description automatically generated


NEW QUESTION # 61
The security team is exploring whether or not the Databricks secrets module can be leveraged for connecting to an external database.
After testing the code with all Python variables being defined with strings, they upload the password to the secrets module and configure the correct permissions for the currently active user. They then modify their code to the following (leaving all other variables unchanged).

Which statement describes what will happen when the above code is executed?

Answer: D

Explanation:
This is the correct answer because the code is using the dbutils.secrets.get method to retrieve the password from the secrets module and store it in a variable. The secrets module allows users to securely store and access sensitive information such as passwords, tokens, or API keys. The connection to the external table will succeed because the password variable will contain the actual password value. However, when printing the password variable, the string "redacted" will be displayed instead of the plain text password, as a security measure to prevent exposing sensitive information in notebooks. Verified References: [Databricks Certified Data Engineer Professional], under "Security & Governance" section; Databricks Documentation, under
"Secrets" section.


NEW QUESTION # 62
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?

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.
Reference: Databricks documentation on Delta Lake, particularly the sections on cloning tables (shallow and deep cloning) and data retention with the vacuum command (https://docs.databricks.com/delta/index.html).


NEW QUESTION # 63
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