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Databricks-Certified-Data-Engineer-Associate PDF題庫，Databricks-Certified-Data-Engineer-Associate證照考試

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最新的 Databricks Certification Databricks-Certified-Data-Engineer-Associate 免費考試真題 (Q58-Q63):

問題 #58
Which of the following commands will return the location of database customer360?

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

答案：C

問題 #59
A data engineer has been given a new record of data:
id STRING = 'a1'
rank INTEGER = 6
rating FLOAT = 9.4
Which of the following SQL commands can be used to append the new record to an existing Delta table my_table?

A. INSERT VALUES ( 'a1' , 6, 9.4) INTO my_table
B. my_table UNION VALUES ('a1', 6, 9.4)
C. INSERT INTO my_table VALUES ('a1', 6, 9.4)
D. UPDATE VALUES ('a1', 6, 9.4) my_table
E. UPDATE my_table VALUES ('a1', 6, 9.4)

答案：C

解題說明：
To append a new record to an existing Delta table, you can use the INSERT INTO statement with the VALUES clause. This statement will insert one or more rows into the table with the specified values. Option A is the only code block that follows this syntax correctly. Option B is incorrect, as it uses the UNION operator, which will return a new table that is the union of two tables, not append to an existing table. Option C is incorrect, as it uses the INSERT VALUES statement, which is not a valid SQL syntax. Option D is incorrect, as it uses the UPDATE statement, which will modify existing rows in the table, not append new rows. Option E is incorrect, as it uses the UPDATE VALUES statement, which is also not a valid SQL syntax. Reference: Insert data into a table using SQL | Databricks on AWS, Insert data into a table using SQL - Azure Databricks, Delta Lake Quickstart - Azure Databricks

問題 #60
A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.
Which of the following describes why Auto Loader inferred all of the columns to be of the string type?

A. Auto Loader only works with string data
B. All of the fields had at least one null value
C. There was a type mismatch between the specific schema and the inferred schema
D. JSON data is a text-based format
E. Auto Loader cannot infer the schema of ingested data

答案：D

問題 #61
A data engineer that is new to using Python needs to create a Python function to add two integers together and return the sum?
Which of the following code blocks can the data engineer use to complete this task?

答案：B

解題說明：
https://www.w3schools.com/python/python_functions.asp
https://www.geeksforgeeks.org/python-functions/

問題 #62
A data engineer has a Job with multiple tasks that runs nightly. Each of the tasks runs slowly because the clusters take a long time to start.
Which of the following actions can the data engineer perform to improve the start up time for the clusters used for the Job?

A. They can configure the clusters to autoscale for larger data sizes
B. They can use clusters that are from a cluster pool
C. They can use endpoints available in Databricks SQL
D. They can configure the clusters to be single-node
E. They can use jobs clusters instead of all-purpose clusters

答案：B

解題說明：
Explanation
Cluster pools are a way to pre-provision clusters that are ready to use. This can reduce the start up time for clusters, as they do not have to be created from scratch. All-purpose clusters are not pre-provisioned, so they will take longer to start up. Jobs clusters are a type of cluster pool, but they are not the best option for this use case. Jobs clusters are designed for long-running jobs, and they can be more expensive than other types of cluster pools. Single-node clusters are the smallest type of cluster, and they will start up the fastest. However, they may not be powerful enough to run the Job's tasks. Autoscaling clusters can scale up or down based on demand. This can help to improve the start up time for clusters, as they will only be created when they are needed. However, autoscaling clusters can also be more expensive than other types of cluster pool
https://docs.databricks.com/en/clusters/pool-best-practices.html

問題 #63
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