Databricks Databricks-Certified-Data-Engineer-Associate 問題集

質問 1：
In order for Structured Streaming to reliably track the exact progress of the processing so that it can handle any kind of failure by restarting and/or reprocessing, which of the following two approaches is used by Spark to record the offset range of the data being processed in each trigger?
A. Replayable Sources and Idempotent Sinks
B. Checkpointing and Idempotent Sinks
C. Write-ahead Logs and Idempotent Sinks
D. Structured Streaming cannot record the offset range of the data being processed in each trigger.
E. Checkpointing and Write-ahead Logs
正解：E
解説: (Topexam メンバーにのみ表示されます)

質問 2：
A data engineer needs to apply custom logic to identify employees with more than 5 years of experience in array column employees in table stores. The custom logic should create a new column exp_employees that is an array of all of the employees with more than 5 years of experience for each row. In order to apply this custom logic at scale, the data engineer wants to use the FILTER higher-order function.
Which of the following code blocks successfully completes this task?

A. Option E
B. Option A
C. Option C
D. Option D
E. Option B
正解：B
解説: (Topexam メンバーにのみ表示されます)

質問 3：
A data engineer has a Job that has a complex run schedule, and they want to transfer that schedule to other Jobs.
Rather than manually selecting each value in the scheduling form in Databricks, which of the following tools can the data engineer use to represent and submit the schedule programmatically?
A. pyspark.sql.types.TimestampType
B. datetime
C. There is no way to represent and submit this information programmatically
D. Cron syntax
E. pyspark.sql.types.DateType
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 4：
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 be single-node
B. They can use endpoints available in Databricks SQL
C. They can use jobs clusters instead of all-purpose clusters
D. They can use clusters that are from a cluster pool
E. They can configure the clusters to autoscale for larger data sizes
正解：D
解説: (Topexam メンバーにのみ表示されます)

質問 5：
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. DELTA
C. autoloader
D. org.apache.spark.sql.sqlite
E. org.apache.spark.sql.jdbc
正解：A
解説: (Topexam メンバーにのみ表示されます)

質問 6：
A data engineering team has noticed that their Databricks SQL queries are running too slowly when they are submitted to a non-running SQL endpoint. The data engineering team wants this issue to be resolved.
Which of the following approaches can the team use to reduce the time it takes to return results in this scenario?
A. They can turn on the Auto Stop feature for the SQL endpoint.
B. They can increase the maximum bound of the SQL endpoint's scaling range
C. They can turn on the Serverless feature for the SQL endpoint and change the Spot Instance Policy to "Reliability Optimized."
D. They can increase the cluster size of the SQL endpoint.
E. They can turn on the Serverless feature for the SQL endpoint.
正解：E
解説: (Topexam メンバーにのみ表示されます)

質問 7：
A data analyst has created a Delta table sales that is used by the entire data analysis team. They want help from the data engineering team to implement a series of tests to ensure the data is clean. However, the data engineering team uses Python for its tests rather than SQL.
Which of the following commands could the data engineering team use to access sales in PySpark?
A. spark.table("sales")
B. spark.sql("sales")
C. SELECT * FROM sales
D. There is no way to share data between PySpark and SQL.
E. spark.delta.table("sales")
正解：A