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| Section | Objectives |
|---|---|
| Data Ingestion and Transformation | - Ingest data using Apache Spark and Databricks - Transform and clean datasets using Spark SQL and DataFrame APIs - Handle batch and streaming data pipelines |
| Production Pipelines and Orchestration | - Automate ETL pipelines and scheduling - Pipeline reliability and fault tolerance - Build and manage workflows using Databricks Jobs |
| Data Modeling and Storage | - Schema evolution and data partitioning strategies - Design scalable data lakehouse architectures - Delta Lake table design and optimization |
| Security, Governance, Monitoring, and Optimization | - Monitor and optimize Spark workloads - Implement Unity Catalog governance and access control - Cost optimization and performance tuning |
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質問 # 136
You are trying to calculate total sales made by all the employees by parsing a complex struct data type that stores employee and sales data, how would you approach this in SQL Table definition, batchId INT, performance ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>, in-sertDate TIMESTAMP Sample data of performance column
1.[
2.{ "employeeId":1234
3."sales" : 10000},
4.
5.{ "employeeId":3232
6."sales" : 30000}
7.]
Calculate total sales made by all the employees?
Sample data with create table syntax for the data:
1.create or replace table sales as
2.select 1 as batchId ,
3.from_json('[{ "employeeId":1234,"sales" : 10000 },{ "employeeId":3232,"sales" : 30000 }]',
4. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
5. current_timestamp() as insertDate
6.union all
7.select 2 as batchId ,
8. from_json('[{ "employeeId":1235,"sales" : 10500 },{ "employeeId":3233,"sales" : 32000 }]',
9. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
10. current_timestamp() as insertDate
正解：E
解説：
Explanation
The answer is
1.select aggregate(flatten(collect_list(performance.sales)), 0, (x, y) -> x + y)
2.as total_sales from sales
Nested Struct can be queried using the . notation performance.sales will give you access to all the sales values in the performance column.
Note: option D is wrong because it uses performance:sales not performance.sales. ":" this is only used when referring to JSON data but here we are dealing with a struct data type. for the exam please make sure to understand if you are dealing with JSON data or Struct data.
Other solutions:
we can also use reduce instead of aggregate
select reduce(flatten(collect_list(performance.sales)), 0, (x, y) -> x + y) as total_sales from sales we can also use explode and sum instead of using any higher-order funtions.
1.with cte as (
2. select
3. explode(flatten(collect_list(performance.sales))) sales from sales
4.)
5.select
6. sum(sales) from cte
Sample data with create table syntax for the data:
1.create or replace table sales as
2.select 1 as batchId ,
3.from_json('[{ "employeeId":1234,"sales" : 10000 },{ "employeeId":3232,"sales" : 30000 }]',
4. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
5. current_timestamp() as insertDate
6.union all
7.select 2 as batchId ,
8. from_json('[{ "employeeId":1235,"sales" : 10500 },{ "employeeId":3233,"sales" : 32000 }]',
9. 'ARRAY<STRUCT<employeeId: BIGINT, sales: INT>>') as performance,
10. current_timestamp() as insertDate
質問 # 137
Which of the following developer operations in CI/CD flow can be implemented in Databricks Re-pos?
正解：C
解説：
Explanation
See the below diagram to understand the role Databricks Repos and Git provider plays when building a CI/CD workflow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure DevOps
質問 # 138
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?
正解：A
解説：
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
質問 # 139
A data engineer is designing a Lakeflow Declarative Pipeline to process streaming order data. The pipeline uses Auto Loader to ingest data and must enforce data quality by ensuring customer_id and amount are greater than zero. Invalid records should be dropped.
Which Lakeflow Declarative Pipelines configurations implement this requirement using Python?
正解：A
解説：
Lakeflow Declarative Pipelines (LDP), formerly Delta Live Tables (DLT), supports enforcing data quality using expectations . Expectations can either:
* Track violations (expect) # records that do not meet conditions are flagged but still included in the pipeline.
* Drop violations (expect_or_drop) # records that do not meet conditions are excluded from downstream tables.
* Fail pipeline on violations (expect_or_fail) # records that fail conditions stop the pipeline.
In this scenario, the requirement explicitly states that invalid records (where customer_id is null or amount #
0) must be dropped . According to the official documentation, the correct method is .expect_or_drop( " expectation_name " , " SQL_predicate " ) applied on the streaming input.
* Option A is correct: It uses .expect_or_drop directly within the transformation chain for both rules, ensuring records that fail are removed before writing to the silver table.
* Option B incorrectly uses @dlt.expect decorators, which only track violations but do not drop invalid rows.
* Option C uses .expect, which also only flags rows, not drop them.
* Option D uses @dlt.expect_or_drop decorator syntax, which is not supported in Python API; expect_or_drop must be applied as a method on the DataFrame, not as a decorator.
Therefore, the correct solution is Option A , which ensures compliance by enforcing data quality and dropping invalid rows programmatically during ingestion.
Reference: Databricks Lakeflow Declarative Pipelines Documentation - Expectations (expect, expect_or_drop, expect_or_fail)
質問 # 140
A user wants to use DLT expectations to validate that a derived table report contains all records from the source, included in the table validation_copy.
The user attempts and fails to accomplish this by adding an expectation to the report table definition.
Which approach would allow using DLT expectations to validate all expected records are present in this table?
正解：C
解説：
To validate that all records from the source are included in the derived table, creating a view that performs a left outer join between the validation_copy table and the report table is effective. The view can highlight any discrepancies, such as null values in the report table's key columns, indicating missing records. This view can then be referenced in DLT (Delta Live Tables) expectations for the report table to ensure data integrity. This approach allows for a comprehensive comparison between the source and the derived table.
:
Databricks Documentation on Delta Live Tables and Expectations: Delta Live Tables Expectations
質問 # 141
......
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