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

Section	Weight	Objectives
Topic 1: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Monitoring and alerting 2. Jobs and job scheduling 3. Task dependencies and orchestration
Topic 2: Data Ingestion	15-20%	- Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals - Batch ingestion methods 1. DBR autoloader 2. Integration with external systems 3. Spark APIs for ingestion
Topic 3: Delta Lake	20-25%	- Delta Lake fundamentals 1. Time travel and data versioning 2. Optimize and Z-order 3. ACID transactions - Delta Lake operations 1. Delta Live Tables 2. Schema evolution and enforcement 3. Merge, update, delete operations
Topic 4: Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Performance optimization techniques 2. Spark APIs in Python 3. Built-in and user-defined functions - Spark DataFrames and Spark SQL 1. DataFrame operations and transformations 2. Spark SQL queries and functions 3. Window functions
Topic 5: 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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q199-Q204):

NEW QUESTION # 199
A data architect has heard about lake's built-in versioning and time travel capabilities. For auditing purposes they have a requirement to maintain a full of all valid street addresses as they appear in the customers table.
The architect is interested in implementing a Type 1 table, overwriting existing records with new values and relying on Delta Lake time travel to support long-term auditing. A data engineer on the project feels that a Type 2 table will provide better performance and scalability.
Which piece of information is critical to this decision?

A. Delta Lake time travel cannot be used to query previous versions of these tables because Type 1 changes modify data files in place.
B. Delta Lake time travel does not scale well in cost or latency to provide a long-term versioning solution.
C. Shallow clones can be combined with Type 1 tables to accelerate historic queries for long-term versioning.
D. Data corruption can occur if a query fails in a partially completed state because Type 2 tables requires Setting multiple fields in a single update.

Answer: B

Explanation:
Delta Lake's time travel feature allows users to access previous versions of a table, providing a powerful tool for auditing and versioning. However, using time travel as a long-term versioning solution for auditing purposes can be less optimal in terms of cost and performance, especially as the volume of data and the number of versions grow. For maintaining a full history of valid street addresses as they appear in a customers table, using a Type 2 table (where each update creates a new record with versioning) might provide better scalability and performance by avoiding the overhead associated with accessing older versions of a large table. While Type 1 tables, where existing records are overwritten with new values, seem simpler and can leverage time travel for auditing, the critical piece of information is that time travel might not scale well in cost or latency for long-term versioning needs, making a Type 2 approach more viable for performance and scalability.
:
Databricks Documentation on Delta Lake's Time Travel: Delta Lake Time Travel Databricks Blog on Managing Slowly Changing Dimensions in Delta Lake: Managing SCDs in Delta Lake

NEW QUESTION # 200
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?

A. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
B. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
C. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
D. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
E. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.

Answer: B

Explanation:
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees. Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs. Verified References: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under
"Choose a region" section.

NEW QUESTION # 201
A company has a task management system that tracks the most recent status of tasks. The system takes task events as input and processes events in near real-time using Lakeflow Declarative Pipelines. A new task event is ingested into the system when a task is created or the task status is changed. Lakeflow Declarative Pipelines provides a streaming table (tasks_status) for BI users to query.
The table represents the latest status of all tasks and includes 5 columns:
* task_id (unique for each task)
* task_name
* task_owner
* task_status
* task_event_time
The table enables three properties: deletion vectors, row tracking, and change data feed (CDF).
A data engineer is asked to create a new Lakeflow Declarative Pipeline to enrich the tasks_status table in near real-time by adding one additional column representing task_owner's department, which can be looked up from a static dimension table (employee).
How should this enrichment be implemented?

A. Create a new Lakeflow Declarative Pipeline: use readStream() function with option readChangeFeed to read tasks_status table CDF; enrich with the employee table; create a new streaming table as the result table and use apply_changes() function to process the changes from the enriched CDF.
B. Create a new Lakeflow Declarative Pipeline: use the readStream() function with the option skipChangeCommits to read the tasks_status table; enrich with the employee table; store the result in a new streaming table.
C. Create a new Lakeflow Declarative Pipeline: use the read() function to read tasks_status table; enrich with employee table; store the result in a materialized view.
D. Create a new Lakeflow Declarative Pipeline: use the readStream() function to read tasks_status table; enrich with the employee table; store the result in a new streaming table.

Answer: A

Explanation:
Change Data Feed (CDF) allows downstream consumers to read incremental changes (inserts, updates, deletes) from a Delta table. The documentation explains that when streaming from a Delta table with CDF enabled, developers can use readStream().option( " readChangeFeed " , " true " ) to capture incremental events. For maintaining a derived table with enrichment logic, the recommended practice is to use apply_changes(), which applies CDC semantics (insert/update/delete) correctly to the target streaming table.
By joining with the static employee dimension, enriched rows are generated before being merged into the new streaming target. This ensures correctness, scalability, and minimal latency. Batch reads or skipping commits do not maintain correctness for CDC pipelines.

NEW QUESTION # 202
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 fail completely because of the constraint violation and no records will be inserted into the target table.
B. The write will include all records in the target table; any violations will be indicated in the boolean column named valid_coordinates.
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 reported in a warning log.
E. The write will insert all records except those that violate the table constraints; the violating records will be recorded to a quarantine table.

Answer: A

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 # 203
The marketing team is launching a new campaign to monitor the performance of the new campaign for the first two weeks, they would like to set up a dashboard with a refresh schedule to run every 5 minutes, which of the below steps can be taken to reduce of the cost of this refresh over time?

A. Reduce the max size of auto scaling from 10 to 5
B. Setup the dashboard refresh schedule to end in two weeks
C. Always use X-small cluster
D. Reduce the size of the SQL Cluster size
E. Change the spot instance policy from reliability optimized to cost optimized

Answer: B

Explanation:
Explanation
The answer is Setup the dashboard refresh schedule to end in two weeks

NEW QUESTION # 204
......

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