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Databricks Certified Data Engineer Professional 認定 Databricks-Certified-Data-Engineer-Professional 試験問題:

1. Which statement describes integration testing?

A) Requires manual intervention
B) Validates an application use case
C) Validates interactions between subsystems of your application
D) Requires an automated testing framework
E) Validates behavior of individual elements of your application

2. An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For auditing purposes, the data governance team wishes to maintain a full record of all values that have ever been valid in the source system. For analytical purposes, only the most recent value for each record needs to be recorded. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A) Use merge into to insert, update, or delete the most recent entry for each pk_id into a bronze table, then propagate all changes throughout the system.
B) Iterate through an ordered set of changes to the table, applying each in turn; rely on Delta Lake's versioning ability to create an audit log.
C) Create a separate history table for each pk_id resolve the current state of the table by running a Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from union all filtering the history tables for the most recent state.
D) Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
E) Ingest all log information into a bronze table; use merge into to insert, update, or delete the most recent entry for each pk_id into a silver table to recreate the current table state.

3. A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on Task A.
If task A fails during a scheduled run, which statement describes the results of this run?

A) Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task A failed, all commits will be rolled back automatically.
B) Tasks B and C will be skipped; some logic expressed in task A may have been committed before task failure.
C) Tasks B and C will be skipped; task A will not commit any changes because of stage failure.
D) Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until all tasks have successfully been completed.
E) Tasks B and C will attempt to run as configured; any changes made in task A will be rolled back due to task failure.

4. The data architect has mandated that all tables in the Lakehouse should be configured as external (also known as "unmanaged") Delta Lake tables.
Which approach will ensure that this requirement is met?

A) When configuring an external data warehouse for all table storage, leverage Databricks for all ELT.
B) When the workspace is being configured, make sure that external cloud object storage has been mounted.
C) When a database is being created, make sure that the LOCATION keyword is used.
D) When data is saved to a table, make sure that a full file path is specified alongside the Delta format.
E) When tables are created, make sure that the EXTERNAL keyword is used in the CREATE TABLE statement.

5. 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?

A) Define a view that performs a left outer join on validation_copy and report, and reference this view in DLT expectations for the report table
B) Define a function that performs a left outer join on validation_copy and report and report, and check against the result in a DLT expectation for the report table
C) Define a temporary table that perform a left outer join on validation_copy and report, and define an expectation that no report key values are null
D) Define a SQL UDF that performs a left outer join on two tables, and check if this returns null values for report key values in a DLT expectation for the report table.

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