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Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q77-Q82):

質問 # 77
A junior developer complains that the code in their notebook isn't producing the correct results in the development environment. A shared screenshot reveals that while they're using a notebook versioned with Databricks Repos, they're using a personal branch that contains old logic. The desired branch named dev-2.3.9 is not available from the branch selection dropdown.
Which approach will allow this developer to review the current logic for this notebook?

A. Merge all changes back to the main branch in the remote Git repository and clone the repo again
B. Use Repos to checkout the dev-2.3.9 branch and auto-resolve conflicts with the current branch
C. Use Repos to make a pull request use the Databricks REST API to update the current branch to dev-2.3.9
D. Use Repos to merge the current branch and the dev-2.3.9 branch, then make a pull request to sync with the remote repository
E. Use Repos to pull changes from the remote Git repository and select the dev-2.3.9 branch.

正解：E

解説：
This is the correct answer because it will allow the developer to update their local repository with the latest changes from the remote repository and switch to the desired branch. Pulling changes will not affect the current branch or create any conflicts, as it will only fetch the changes and not merge them. Selecting the dev-2.3.9 branch from the dropdown will checkout that branch and display its contents in the notebook.

質問 # 78
A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns. The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB. How should the data engineer meet this requirement?

A. Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.
B. Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.
C. Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.
D. Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.

正解：C

解説：
Liquid clustering is designed for managed tables under 1TB with evolving query patterns. It efficiently addresses data skew, continuously optimizes data layout, and allows clustering keys to be changed without requiring full data rewrites, making it well suited for frequently changing filter columns while minimizing maintenance overhead.

質問 # 79
The data engineering team maintains the following code:
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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. 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.
C. An incremental job will leverage running information in the state store to update aggregate values in the gold_customer_lifetime_sales_summary table.
D. 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.
E. 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.

正解：B

解説：
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.

質問 # 80
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 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.
B. 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
C. 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
D. 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

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.

質問 # 81
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When tables are created, make sure that the external keyword is used in the create table statement.
B. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
C. Whenever a database is being created, make sure that the location keyword is used Get Latest & Actual Certified-Data-Engineer-Professional Exam's Question and Answers from
D. When the workspace is being configured, make sure that external cloud object storage has been mounted.
E. Whenever a table is being created, make sure that the location keyword is used.

正解：E

解説：
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it.

質問 # 82
......

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信頼的なDatabricks-Certified-Data-Engineer-Professional問題トレーリング一回合格-高品質なDatabricks-Certified-Data-Engineer-Professional学習体験談

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Databricks Databricks-Certified-Data-Engineer-Professional学習体験談、Databricks-Certified-Data-Engineer-Professional学習資料

Databricks Certified Data Engineer Professional Exam 認定 Databricks-Certified-Data-Engineer-Professional 試験問題 (Q77-Q82):

便利なDatabricks-Certified-Data-Engineer-Professional問題トレーリング & 合格スムーズDatabricks-Certified-Data-Engineer-Professional学習体験談 | 検証するDatabricks-Certified-Data-Engineer-Professional学習資料

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