Databricks-Certified-Data-Engineer-Associate練習問題、Databricks-Certified-Data-Engineer-Associate技術内容

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

Section	Weight	Objectives
Topic 1: Python for Data Engineering	10-15%	- Implement user-defined functions (UDFs) - Work with Spark APIs in Python - Use PySpark for data processing
Topic 2: Lakehouse Platform Concepts	10-15%	- Explain data governance and security concepts - Describe key Databricks Lakehouse platform components - Understand the Lakehouse architecture and its benefits
Topic 3: Apache Spark Data Processing Fundamentals	20-25%	- Work with structured data types (arrays, maps, structs) - Apply transformations and actions on DataFrames - Create and use Spark DataFrames - Use Spark SQL for data processing
Topic 4: Data Pipeline Architecture	15-20%	- Monitor and optimize pipeline performance - Understand ELT vs ETL patterns - Design data pipelines for batch and streaming - Implement incremental data processing
Topic 5: Delta Lake Fundamentals	20-25%	- Write to and read from Delta tables - Understand ACID transactions and time travel - Explain Delta Lake features and benefits - Create and manage Delta tables
Topic 6: Spark SQL and DataFrames	15-20%	- Aggregate and group data - Write and execute Spark SQL queries - Join and union DataFrames - Handle null values and data quality

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Databricks-Certified-Data-Engineer-Associate技術内容 & Databricks-Certified-Data-Engineer-Associateシュミレーション問題集

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Databricks Certified Data Engineer Associate Exam 認定 Databricks-Certified-Data-Engineer-Associate 試験問題 (Q46-Q51):

質問 # 46
Which of the following statements regarding the relationship between Silver tables and Bronze tables is always true?

A. Silver tables contain more data than Bronze tables.
B. Silver tables contain aggregates while Bronze data is unaggregated.
C. Silver tables contain a less refined, less clean view of data than Bronze data.
D. Silver tables contain less data than Bronze tables.
E. Silver tables contain a more refined and cleaner view of data than Bronze tables.

正解：E

解説：
In a medallion architecture, a common data design pattern for lakehouses, data flows from Bronze to Silver to Gold layer tables, with each layer progressively improving the structure and quality of data. Bronze tables store raw data ingested from various sources, while Silver tables apply minimal transformations and cleansing to create an enterprise view of the data. Silver tables can also join and enrich data from different Bronze tables to provide a more complete and consistent view of the data. Therefore, option D is the correct answer, as Silver tables contain a more refined and cleaner view of data than Bronze tables. Option A is incorrect, as it is the opposite of the correct answer. Option B is incorrect, as Silver tables do not necessarily contain aggregates, but can also store detailed records. Option C is incorrect, as Silver tables may contain less data than Bronze tables, depending on the transformations and cleansing applied. Option E is incorrect, as Silver tables may contain more data than Bronze tables, depending on the joins and enrichments applied. Reference: What is a Medallion Architecture?, Transforming Bronze Tables in Silver Tables, What is the medallion lakehouse architecture?

質問 # 47
A data engineer is decommissioning a sandbox schema in Unity Catalog. Some tables are ephemeral staging outputs that can be safety removed entirely, but a few tables point at shared cloud storage used by downstream jobs outside Databricks. The engineer must avoid deleting any shared files when cleaning up catalog objects.
How does Unity Catalog behave when dropping Managed vs External tables?

A. DROP managed tables that are ephemeral and DROP external tables; files for both remain for 7 days.
B. DROP managed staging tables to remove data and metadata, and DROP external tables to remove only metadata
C. DROP external tables first to delete their files, then DROP managed tables to keep their files for recovery.
D. DROP all tables; Databricks will only remove metadata for both managed and external tables.

正解：B

解説：
In Unity Catalog, dropping a managed table deletes both the table metadata and the underlying data files, while dropping an external table removes only the catalog metadata and leaves the referenced cloud storage data intact.

質問 # 48
A data engineer needs to create a table in Databricks using data from their organization's existing SQLite database.
They run the following command:

Which of the following lines of code fills in the above blank to successfully complete the task?

A. sqlite
B. org.apache.spark.sql.sqlite
C. DELTA
D. org.apache.spark.sql.jdbc
E. autoloader

正解：D

質問 # 49
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 automatically saves development progress
B. Databricks Repos is wholly housed within the Databricks Lakehouse Platform
C. Databricks Repos allows users to revert to previous versions of a notebook
D. Databricks Repos provides the ability to comment on specific changes
E. Databricks Repos supports the use of multiple branches

正解：E

解説：
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as cloning, committing, pushing, pulling, and branch management. Databricks Notebooks versioning is a legacy feature that allows users to link notebooks to GitHub repositories and perform basic Git operations. However, Databricks Notebooks versioning does not support the use of multiple branches for development work, which is an advantage of using Databricks Repos. With Databricks Repos, users can create and manage branches for different features, experiments, or bug fixes, and merge, rebase, or resolve conflicts between them. Databricks recommends using a separate branch for each notebook and following data science and engineering code development best practices using Git for version control, collaboration, and CI/CD. References: Git integration with Databricks Repos - Azure Databricks | Microsoft Learn, Git version control for notebooks (legacy) | Databricks on AWS, Databricks Repos Is Now Generally Available - New 'Files' Feature in ..., Databricks Repos - What it is and how we can use it | Adatis.

質問 # 50
A data engineer has three tables in a Delta Live Tables (DLT) pipeline. They have configured the pipeline to drop invalid records at each table. They notice that some data is being dropped due to quality concerns at some point in the DLT pipeline. They would like to determine at which table in their pipeline the data is being dropped.
Which of the following approaches can the data engineer take to identify the table that is dropping the records?

A. They can navigate to the DLT pipeline page, click on each table, and view the data quality statistics.
B. They can navigate to the DLT pipeline page, click on the "Error" button, and review the present errors.
C. They can set up DLT to notify them via email when records are dropped.
D. They can set up separate expectations for each table when developing their DLT pipeline.
E. They cannot determine which table is dropping the records.

正解：A

解説：
One of the features of DLT is that it provides data quality metrics for each dataset in the pipeline, such as the number of records that pass or fail expectations, the number of records that are dropped, and the number of records that are written to the target. These metrics can be accessed from the DLT pipeline page, where the data engineer can click on each table and view the data quality statistics for the latest update or any previous update. This way, they can identify which table is dropping the records and why. Reference:
Monitor Delta Live Tables pipelines
Manage data quality with Delta Live Tables

質問 # 51
......

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