Databricks Databricks-Certified-Data-Engineer-Associate Exam Dumps

Databricks Certified Data Engineer Associate Exam

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Databricks Databricks-Certified-Data-Engineer-Associate Sample Questions

Question # 1

In which of the following scenarios should a data engineer select a Task in the Depends On field of a new Databricks Job Task? 

A. When another task needs to be replaced by the new task  
B. When another task needs to fail before the new task begins  
C. When another task has the same dependency libraries as the new task  
D. When another task needs to use as little compute resources as possible  
E. When another task needs to successfully complete before the new task begins  

Answer: E

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Question # 2

Which of the following commands will return the location of database customer360? 

A. DESCRIBE LOCATION customer360;  
B. DROP DATABASE customer360;  
C. DESCRIBE DATABASE customer360;  
D. ALTER DATABASE customer360 SET DBPROPERTIES ('location' = '/user'};  
E. USE DATABASE customer360;  

Answer: C

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Question # 3

A data engineer has created a new database using the following command: CREATE DATABASE IF NOT EXISTS customer360;In which of the following locations will the customer360 database be located?  

A. dbfs:/user/hive/database/customer360  
B. dbfs:/user/hive/warehouse  
C. dbfs:/user/hive/customer360  
D. More information is needed to determine the correct response  

Answer: B

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Question # 4

Which of the following approaches should be used to send the Databricks Job owner an email in the case that the Job fails?

A. Manually programming in an alert system in each cell of the Notebook  
B. Setting up an Alert in the Job page  
C. Setting up an Alert in the Notebook  
D. There is no way to notify the Job owner in the case of Job failure  
E. MLflow Model Registry Webhooks  

Answer: B

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Question # 5

A data engineering team has two tables. The first table march_transactions is a collection of all retail transactions in the month of March. The second table april_transactions is a collection of all retail transactions in the month of April. There are no duplicate records between the tables. Which of the following commands should be run to create a new table all_transactions that contains all records from march_transactions and april_transactions without duplicate records?

A. CREATE TABLE all_transactions AS SELECT * FROM march_transactions INNER JOIN SELECT * FROM april_transactions;
B.CREATE TABLE all_transactions AS SELECT * FROM march_transactions UNION SELECT * FROM april_transactions;   
C.CREATE TABLE all_transactions AS SELECT * FROM march_transactions OUTER JOIN SELECT * FROM april_transactions;     
D.CREATE TABLE all_transactions AS SELECT * FROM march_transactions INTERSECT SELECT * from april_transactions;     
E.CREATE TABLE all_transactions AS SELECT * FROM march_transactions MERGE SELECT * FROM april_transactions;     

Answer: B

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Question # 6

Which of the following describes when to use the CREATE STREAMING LIVE TABLE (formerly CREATE INCREMENTAL LIVE TABLE) syntax over the CREATE LIVE TABLE syntax when creating Delta Live Tables (DLT) tables using SQL?

A. CREATE STREAMING LIVE TABLE should be used when the subsequent step in the DLT pipeline is static
B. CREATE STREAMING LIVE TABLE should be used when data needs to be processed incrementally
C. CREATE STREAMING LIVE TABLE is redundant for DLT and it does not need to be used.
D. CREATE STREAMING LIVE TABLE should be used when data needs to be processed through complicated aggregations. 
E. CREATE STREAMING LIVE TABLE should be used when the previous step in the DLT pipeline is static.

Answer: B

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Question # 7

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. spark.sql  
D. There is no way to share data between PySpark and SQL.  
E. spark.table  

Answer: C

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Question # 8

A data analysis team has noticed that their Databricks SQL queries are running too slowly when connected to their always-on SQL endpoint. They claim that this issue is present when many members of the team are running small queries simultaneously. They ask the data engineering team for help. The data engineering team notices that each of the team’s queries uses the same SQL endpoint. Which of the following approaches can the data engineering team use to improve the latency of the team’s queries?

A. They can increase the cluster size of the SQL endpoint.  
B. They can increase the maximum bound of the SQL endpoint’s scaling range.  
C. They can turn on the Auto Stop feature for the SQL endpoint.  
D. They can turn on the Serverless feature for the SQL endpoint.  
E. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to “Reliability Optimized.” 

Answer: A

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Question # 9

A data organization leader is upset about the data analysis team’s reports being different from the data engineering team’s reports. The leader believes the siloed nature of their organization’s data engineering and data analysis architectures is to blame.Which of the following describes how a data lakehouse could alleviate this issue?  

A. Both teams would autoscale their work as data size evolves  
B. Both teams would use the same source of truth for their work  
C. Both teams would reorganize to report to the same department  
D. Both teams would be able to collaborate on projects in real-time  
E. Both teams would respond more quickly to ad-hoc requests  

Answer: B

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