最新のCertified-Data-Engineer-Professional問題集で試験を準備する

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

問題 #1

A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
B. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
C. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
D. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.

問題 #2

A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

A. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.
B. Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
C. Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
D. Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
E. Both commands will fail. No new variables, tables, or views will be created.

問題 #3

A platform team lead is responsible for automating the individual teams attribution towards SQL Warehouse usage. The requirement is to identify the SQL warehouse usage at the individual user's level and generate a daily report to be shared with an executive team that includes leaders from all business units. How should the platform lead generate an automated report that can be shared daily?

A. Use the system tables to capture the audit and billing usage data and share the queries with the executive team. This enables the executives to execute the query and see the latest results any time.
B. Restrict users from running any SQL query unless they provide all the query details so that the attribution can be calculated and shared with the executive team.
C. Use the system tables to capture the audit and billing usage data and create a dashboard with daily refresh schedules and shared with the executive team.
D. Let the users run the SQL query and then directly report the usage to the executives. The ownership of the SQL warehouse usage will be with the individual teams.

問題 #4

Two of the most common data locations on Databricks are the DBFS root storage and external object storage mounted with dbutils.fs.mount().
Which of the following statements is correct?

A. The DBFS root is the most secure location to store data, because mounted storage volumes must have full public read and write permissions.
B. Neither the DBFS root nor mounted storage can be accessed when using %sh in a Databricks notebook.
C. By default, both the DBFS root and mounted data sources are only accessible to workspace administrators.
D. DBFS is a file system protocol that allows users to interact with files stored in object storage using syntax and guarantees similar to Unix file systems.
E. The DBFS root stores files in ephemeral block volumes attached to the driver, while mounted directories will always persist saved data to external storage between sessions.

問題 #5

Which of the following is true of Delta Lake and the Lakehouse?

A. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
B. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
C. Z-order can only be applied to numeric values stored in Delta Lake tables
D. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
E. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.

解説：