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Databricks-Certified-Data-Engineer-Associate Accurate Answers, Databricks-Certified-Data-Engineer-Associate Practice Test Engine

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The Databricks Databricks-Certified-Data-Engineer-Associate Exam covers a range of topics, including data ingestion, processing, and storage. Candidates are tested on their ability to design and implement data pipelines, data models, and data processing workflows using Databricks. Databricks is a cloud-based data platform that provides a unified analytics platform for data engineering, data science, and machine learning.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q31-Q36):

NEW QUESTION # 31
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then perform a streaming write into a new table.
The code block used by the data engineer is below:

If the data engineer only wants the query to process all of the available data in as many batches as required, which of the following lines of code should the data engineer use to fill in the blank?

A. trigger(processingTime="once")
B. trigger(continuous="once")
C. trigger(parallelBatch=True)
D. trigger(availableNow=True)
E. processingTime(1)

Answer: D

Explanation:
Explanation
https://stackoverflow.com/questions/71061809/trigger-availablenow-for-delta-source-streaming-queries-in-pyspa

NEW QUESTION # 32
A data engineer is using the following code block as part of a batch ingestion pipeline to read from a composable table:

Which of the following changes needs to be made so this code block will work when the transactions table is a stream source?

A. Replace schema(schema) with option ("maxFilesPerTrigger", 1)
B. Replace "transactions" with the path to the location of the Delta table
C. Replace spark.read with spark.readStream
D. Replace format("delta") with format("stream")
E. Replace predict with a stream-friendly prediction function

Answer: C

Explanation:
Explanation
https://docs.databricks.com/en/structured-streaming/delta-lake.html
In the context of Databricks, when transitioning from batch processing to stream processing, one common change that needs to be made is replacing spark.read with spark.readStream. This modification is essential because spark.read is used for batch processing, while spark.readStream is used for stream processing. The rest of the code can often remain the same or require minimal changes. References: The information can be referenced from Databricks documentation on structured streaming: Structured Streaming Programming Guide.

NEW QUESTION # 33
Which of the following must be specified when creating a new Delta Live Tables pipeline?

A. A location of a target database for the written data
B. A path to cloud storage location for the written data
C. At least one notebook library to be executed
D. A key-value pair configuration
E. The preferred DBU/hour cost

Answer: C

Explanation:
Explanation
https://docs.databricks.com/en/delta-live-tables/tutorial-pipelines.html

NEW QUESTION # 34
An engineering manager wants to monitor the performance of a recent project using a Databricks SQL query. For the first week following the project's release, the manager wants the query results to be updated every minute. However, the manager is concerned that the compute resources used for the query will be left running and cost the organization a lot of money beyond the first week of the project's release.
Which of the following approaches can the engineering team use to ensure the query does not cost the organization any money beyond the first week of the project's release?

A. They cannot ensure the query does not cost the organization money beyond the first week of the project's release.
B. They can set a limit to the number of DBUs that are consumed by the SQL Endpoint.
C. They can set a limit to the number of individuals that are able to manage the query's refresh schedule.
D. They can set the query's refresh schedule to end after a certain number of refreshes.
E. They can set the query's refresh schedule to end on a certain date in the query scheduler.

Answer: E

Explanation:
In Databricks SQL, you can use scheduled query executions to update your dashboards or enable routine alerts. By default, your queries do not have a schedule. To set the schedule, you can use the dropdown pickers to specify the frequency, period, starting time, and time zone. You can also choose to end the schedule on a certain date by selecting the End date checkbox and picking a date from the calendar. This way, you can ensure that the query does not run beyond the first week of the project's release and does not incur any additional cost. Option A is incorrect, as setting a limit to the number of DBUs does not stop the query from running. Option B is incorrect, as there is no option to end the schedule after a certain number of refreshes. Option C is incorrect, as there is a way to ensure the query does not cost the organization money beyond the first week of the project's release. Option D is incorrect, as setting a limit to the number of individuals who can manage the query's refresh schedule does not affect the query's execution or cost. Reference: Schedule a query, Schedule a query - Azure Databricks - Databricks SQL

NEW QUESTION # 35
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 was managed
B. The table's data was smaller than 10 GB
C. The table did not have a location
D. The table was external
E. The table's data was larger than 10 GB

Answer: D

Explanation:
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. References: Databricks Documentation - Managed and External Tables, Databricks Documentation - Drop Table

NEW QUESTION # 36
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