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

Section	Weight	Objectives
Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Task dependencies and orchestration 2. Monitoring and alerting 3. Jobs and job scheduling
Data Ingestion	15-20%	- Streaming ingestion 1. Structured streaming fundamentals 2. Kafka integration - Batch ingestion methods 1. Spark APIs for ingestion 2. DBR autoloader 3. Integration with external systems
Data Processing with Spark	25-30%	- Spark DataFrames and Spark SQL 1. Window functions 2. DataFrame operations and transformations 3. Spark SQL queries and functions - Python and SQL for data engineering 1. Built-in and user-defined functions 2. Performance optimization techniques 3. Spark APIs in Python
Delta Lake	20-25%	- Delta Lake fundamentals 1. ACID transactions 2. Time travel and data versioning 3. Optimize and Z-order - Delta Lake operations 1. Schema evolution and enforcement 2. Delta Live Tables 3. Merge, update, delete operations
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 (Q209-Q214):

NEW QUESTION # 209
The data engineering team maintains the following code:

Assuming that this code produces logically correct results and the data in the source table has been de-duplicated and validated, which statement describes what will occur when this code is executed?

A. A batch job will update the gold_customer_lifetime_sales_summary table, replacing only those rows that have different values than the current version of the table, using customer_id as the primary key.
B. An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.
C. The gold_customer_lifetime_sales_summary table will be overwritten by aggregated values calculated from all records in the silver_customer_sales table as a batch job.
D. The silver_customer_sales table will be overwritten by aggregated values calculated from all records in the gold_customer_lifetime_sales_summary table as a batch job.
E. An incremental job will detect if new rows have been written to the silver_customer_sales table; if new rows are detected, all aggregates will be recalculated and used to overwrite the gold_customer_lifetime_sales_summary table.

Answer: C

Explanation:
This code is using the pyspark.sql.functions library to group the silver_customer_sales table by customer_id and then aggregate the data using the minimum sale date, maximum sale total, and sum of distinct order ids. The resulting aggregated data is then written to the gold_customer_lifetime_sales_summary table, overwriting any existing data in that table. This is a batch job that does not use any incremental or streaming logic, and does not perform any merge or update operations. Therefore, the code will overwrite the gold table with the aggregated values from the silver table every time it is executed. Reference:
https://docs.databricks.com/spark/latest/dataframes-datasets/introduction-to-dataframes-python.html
https://docs.databricks.com/spark/latest/dataframes-datasets/transforming-data-with-dataframes.html
https://docs.databricks.com/spark/latest/dataframes-datasets/aggregating-data-with-dataframes.html

NEW QUESTION # 210
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: C

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 # 211
Which of the following SQL command can be used to insert or update or delete rows based on a condition to check if a row(s) exists?

A. INSERT INTO OVERWRITE table_name
B. MERGE INTO table_name
C. UPDATE table_name
D. COPY INTO table_name
E. INSERT IF EXISTS table_name

Answer: B

Explanation:
Explanation
here is the additional documentation for your review.
https://docs.databricks.com/spark/latest/spark-sql/language-manual/delta-merge-into.html
1.MERGE INTO target_table_name [target_alias]
2. USING source_table_reference [source_alias]
3. ON merge_condition
4. [ WHEN MATCHED [ AND condition ] THEN matched_action ] [...]
5. [ WHEN NOT MATCHED [ AND condition ] THEN not_matched_action ] [...]
6.
7.matched_action
8. { DELETE |
9. UPDATE SET * |
10. UPDATE SET { column1 = value1 } [, ...] }
11.
12.not_matched_action
13. { INSERT * |
14. INSERT (column1 [, ...] ) VALUES (value1 [, ...])

NEW QUESTION # 212
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
B. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
C. Populate the dashboard by configuring a nightly batch job to save the required to quickly update the dashboard with each query.
D. Define a view against the products_per_order table and define the dashboard against this view.

Answer: D

Explanation:
Given the requirement for daily refresh of data and the need to ensure quick response times for interactive queries while controlling costs, a nightly batch job to pre-compute and save the required summary metrics is the most suitable approach.
By pre-aggregating data during off-peak hours, the dashboard can serve queries quickly without requiring on-the-fly computation, which can be resource-intensive and slow, especially with many users.
This approach also limits the cost by avoiding continuous computation throughout the day and instead leverages a batch process that efficiently computes and stores the necessary data.
The other options (A, C, D) either do not address the cost and performance requirements effectively or are not suitable for the use case of less frequent data refresh and high interactivity.
Reference:
Databricks Documentation on Batch Processing: Databricks Batch Processing Data Lakehouse Patterns: Data Lakehouse Best Practices

NEW QUESTION # 213
A data engineer is designing an append-only pipeline that needs to handle both batch and streaming data in Delta Lake. The team wants to ensure that the streaming component can efficiently track which data has already been processed.
Which configuration should be set to enable this?

A. overwriteSchema
B. checkpointLocation
C. partitionBy
D. mergeSchema

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
When working with Delta Lake streaming ingestion, checkpointing is critical for maintaining fault tolerance and ensuring exactly-once data processing semantics.
The checkpointLocation parameter defines the directory where Spark Structured Streaming stores progress information, offsets, and metadata. This allows the engine to resume processing from the last committed offset without reprocessing previously ingested data.
Without checkpointing, each stream restart would reprocess all data, leading to duplicates. Parameters like partitionBy or schema options (mergeSchema / overwriteSchema) affect table structure, not data lineage tracking. Therefore, the correct and required configuration for efficient streaming state management is checkpointLocation.

NEW QUESTION # 214
......

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