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Databricks Databricks-Certified-Data-Engineer-Associate 認定試験の出題範囲：

トピック	出題範囲
トピック 1	Production Pipelines: It focuses on identifying the advantages of using multiple tasks in Jobs, a suitable scenario where predecessor task should be set up, CRON as an opportunity for scheduling opportunity, and how an alert can be sent via email. The topic also discusses setting up a predecessor task in Jobs, reviewing a task's execution history, and debugging a failed task. Lastly, it delves into setting up a retry policy in case of failure and creating an alert in the case of a failed task.
トピック 2	Databricks Lakehouse Platform: This topic covers the relationship between the data lakehouse and the data warehouse, the improvement in data quality, comparing and contrasting silver and gold tables, elements of the Databricks Platform Architecture, and differentiating between all-purpose clusters and jobs clusters. Moreover, it identifies how cluster software is versioned, how clusters can be filtered, how to use multiple languages, how to run one notebook, how notebooks can be shared, Git operations, and limitations in Databricks Notebooks. Lastly, the topic describes how clusters are terminated, how to use multiple languages, and how Databricks Repos enables CI CD workflows.
トピック 3	ELT with Apache Spark: It focuses on extracting data, identifying the prefix, creating a view, duplicating rows, creating a new table, utilizing the dot, parsing JSON, and defining a SQL UDF. Moreover, the topic delves into describing the security model, identifying the location of a function, and identifying the PIVOT.
トピック 4	Data Governance: It identifies one of the four areas of data governance, Unity Catalog securables, and the cluster security modes. It also discusses how to create a UC-enabled all-purpose cluster and a DBSQL warehouse. The topic explains how to implement data object access control, create a DBSQL warehouse, and e a UC-enabled all-purpose cluster.
トピック 5	Incremental Data Processing: In this topic questions about identifying Delta Lake, benefits of ACID transactions, a scenario to use an external table, location of a table, the benefits of Zordering, the kind of files, CTAS as a solution, the impact of ON VIOLATION DROP ROW and ON VIOLATION FAIL UPDATE, and the necessary component to create a new DLT pipeline. Moreover, the topic also discusses directory structure of Delta Lake files, generated column, adding a table comment, and the benefits of the MERGE command.

参照：https://www.databricks.com/learn/certification/data-engineer-associate

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

問題 #1
A data analyst has developed a query that runs against Delta table. They want help from the data engineering team to implement a series of tests to ensure the data returned by the query is clean.
However, the data engineering team uses Python for its tests rather than SQL.
Which of the following operations could the data engineering team use to run the query and operate with the results in PySpark?

A. SELECT * FROM sales
B. spark.delta.table
C. There is no way to share data between PySpark and SQL.
D. spark.table
E. spark.sql

問題 #2
A new data engineering team team. has been assigned to an ELT project. The new data engineering team will need full privileges on the database customers to fully manage the project.
Which of the following commands can be used to grant full permissions on the database to the new data engineering team?

A. GRANT ALL PRIVILEGES ON DATABASE team TO customers;
B. GRANT SELECT PRIVILEGES ON DATABASE customers TO teams;
C. GRANT ALL PRIVILEGES ON DATABASE customers TO team;
D. GRANT USAGE ON DATABASE customers TO team;
E. GRANT SELECT CREATE MODIFY USAGE PRIVILEGES ON DATABASE customers TO team;

問題 #3
A data engineer is attempting to write Python and SQL in the same command cell and is running into an error. The engineer thought that it was possible to use a Python variable in a select statement. Why does the command fail?

A. Databricks supports multiple languages but only one per notebook.
B. Databricks supports language interoperability but only if a special character is used.
C. Databricks supports language interoperability in the same cell but only between Scala and SQL.
D. Databricks supports one language per cell.

問題 #4
A data engineer needs to determine whether to use the built-in Databricks Notebooks versioning or version their project using Databricks Repos.
Which of the following is an advantage of using Databricks Repos over the Databricks Notebooks versioning?

A. Databricks Repos allows users to revert to previous versions of a notebook
B. Databricks Repos supports the use of multiple branches
C. Databricks Repos is wholly housed within the Databricks Data Intelligence Platform
D. Databricks Repos provides the ability to comment on specific changes
E. Databricks Repos automatically saves development progress

問題 #5
A data engineer works for an organization that must meet a stringent Service Level Agreement (SLA) that demands minimal runtime errors and high availability for its data processing pipelines.
The data engineer wants to avoid the operational overhead of managing and tuning clusters.
Which architectural solution will meet the requirements?

A. Implement a hybrid approach with scheduled batch jobs on custom cloud VMs.
B. Deploy a dedicated, manually managed cluster optimized by in-house IT staff.
C. Use an auto-scaling cluster configured and monitored by the user.
D. Utilize Databricks serverless compute that automatically optimizes resources and abstracts cluster management.

解説：

問題 #1
正解： E

問題 #2
正解： C

問題 #3
正解： D

問題 #4
正解： B

問題 #5
正解： D