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Databricks-Certified-Data-Engineer-Associate考題寶典：Databricks Certified Data Engineer Associate Exam100％通過考試｜最新Databricks-Certified-Data-Engineer-Associate題庫

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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Associate 免費考試真題 (Q103-Q108):

問題 #103
Which of the following Structured Streaming queries is performing a hop from a Silver table to a Gold table?

答案：E

解題說明：
The best practice is to use "Complete" as output mode instead of "append" when working with aggregated tables. Since gold layer is work final aggregated tables, the only option with output mode as complete is option E.

問題 #104
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 supports the use of multiple branches
B. Databricks Repos is wholly housed within the Databricks Lakehouse Platform
C. Databricks Repos provides the ability to comment on specific changes
D. Databricks Repos automatically saves development progress
E. Databricks Repos allows users to revert to previous versions of a notebook

答案：A

問題 #105
Identify how the count_if function and the count where x is null can be used Consider a table random_values with below data.
What would be the output of below query?
select count_if(col > 1) as count_
a. count(*) as count_b.count(col1) as count_c from random_values col1
0
1
2
NULL -
2
3

A. 4 6 6
B. 4 6 5
C. 3 6 6
D. 3 6 5

答案：D

問題 #106
A data engineer is attempting to drop a Spark SQL table my_table. The data engineer wants to delete all table metadata and data.
They run the following command:
DROP TABLE IF EXISTS my_table
While the object no longer appears when they run SHOW TABLES, the data files still exist.
Which of the following describes why the data files still exist and the metadata files were deleted?

A. The table's data was larger than 10 GB
B. The table's data was smaller than 10 GB
C. The table was external
D. The table was managed
E. The table did not have a location

答案：C

解題說明：
An external table is a table that is defined in the metastore and points to an existing location in the storage system. When you drop an external table, only the metadata is deleted from the metastore, but the data files are not deleted from the storage system. This is because external tables are meant to be shared by multiple applications and users, and dropping them should not affect the data availability. On the other hand, a managed table is a table that is defined in the metastore and also managed by the metastore. When you drop a managed table, both the metadata and the data files are deleted from the metastore and the storage system, respectively. This is because managed tables are meant to be exclusive to the application or user that created them, and dropping them should free up the storage space. Therefore, the correct answer is C, because the table was external and only the metadata was deleted when the table was dropped. Reference: Databricks Documentation - Managed and External Tables, Databricks Documentation - Drop Table

問題 #107
A data engineer needs to create a table in Databricks using data from a CSV file at location /path/to/csv.
They run the following command:

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

A. USING DELTA
B. None of these lines of code are needed to successfully complete the task
C. USING CSV
D. FROM CSV
E. FROM "path/to/csv"

答案：E

解題說明：
A data lakehouse is a new paradigm that can be used to simplify and unify siloed data architectures that are specialized for specific use cases. A data lakehouse combines the best of both data lakes and data warehouses, providing a single platform that supports diverse data types, open standards, low-cost storage, high-performance queries, ACID transactions, schema enforcement, and governance. A data lakehouse enables data engineers to build reliable and scalable data pipelines that can serve various downstream applications and users, such as data science, machine learning, analytics, and reporting. A data lakehouse leverages the power of Delta Lake, a storage layer that brings reliability and performance to data lakes. References: What is a data lakehouse?, Delta Lake, Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics

問題 #108
......

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