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Databricks Certified Data Engineer Associate Exam Sample Questions (Q70-Q75):

NEW QUESTION # 70
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 can set a limit to the number of DBUs that are consumed by the SQL Endpoint.
  • B. They can set the query's refresh schedule to end on a certain date in the query scheduler.
  • C. They can set the query's refresh schedule to end after a certain number of refreshes.
  • D. They cannot ensure the query does not cost the organization money beyond the first week of the project's release.
  • E. They can set a limit to the number of individuals that are able to manage the query's refresh schedule.

Answer: B

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 # 71
A data engineer and data analyst are working together on a data pipeline. The data engineer is working on the raw, bronze, and silver layers of the pipeline using Python, and the data analyst is working on the gold layer of the pipeline using SQL. The raw source of the pipeline is a streaming input. They now want to migrate their pipeline to use Delta Live Tables.
Which of the following changes will need to be made to the pipeline when migrating to Delta Live Tables?

  • A. The pipeline will need to stop using the medallion-based multi-hop architecture
  • B. The pipeline will need to use a batch source in place of a streaming source
  • C. The pipeline will need to be written entirely in Python
  • D. The pipeline will need to be written entirely in SQL
  • E. None of these changes will need to be made

Answer: E

Explanation:
Delta Live Tables is a declarative framework for building reliable, maintainable, and testable data processing pipelines. You define the transformations to perform on your data and Delta Live Tables manages task orchestration, cluster management, monitoring, data quality, and error handling. Delta Live Tables supports both SQL and Python as the languages for defining your datasets and expectations. Delta Live Tables also supports both streaming and batch sources, and can handle both append-only and upsert data patterns. Delta Live Tables follows the medallion lakehouse architecture, which consists of three layers of data: bronze, silver, and gold. Therefore, migrating to Delta Live Tables does not require any of the changes listed in the options B, C, D, or E. The data engineer and data analyst can use the same languages, sources, and architecture as before, and simply declare their datasets and expectations using Delta Live Tables syntax. References:
* What is Delta Live Tables?
* Transform data with Delta Live Tables
* What is the medallion lakehouse architecture?


NEW QUESTION # 72
A single Job runs two notebooks as two separate tasks. A data engineer has noticed that one of the notebooks is running slowly in the Job's current run. The data engineer asks a tech lead for help in identifying why this might be the case.
Which of the following approaches can the tech lead use to identify why the notebook is running slowly as part of the Job?

  • A. They can navigate to the Runs tab in the Jobs UI to immediately review the processing notebook.
  • B. They can navigate to the Runs tab in the Jobs UI and click on the active run to review the processing notebook.
  • C. They can navigate to the Tasks tab in the Jobs UI and click on the active run to review the processing notebook.
  • D. They can navigate to the Tasks tab in the Jobs UI to immediately review the processing notebook.
  • E. There is no way to determine why a Job task is running slowly.

Answer: B


NEW QUESTION # 73
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. Cron syntax
  • B. There is no way to represent and submit this information programmatically
  • C. pyspark.sql.types.TimestampType
  • D. datetime
  • E. pyspark.sql.types.DateType

Answer: A

Explanation:
Cron syntax is a tool that can be used to represent and submit a complex run schedule programmatically. Cron syntax is a string of six fields that specify the frequency, date, and time of a job run. For example, the cron expression 0 0 12 * * ? means run the job at 12:00 PM every day. The data engineer can use the Databricks REST API to create or update a job with a cron schedule. The data engineer can also use the Databricks CLI to create or update a job with a cron schedule by using a JSON file that contains the cron expression. The other tools are either invalid or not suitable for representing and submitting a complex run schedule programmatically. References: Schedule a job, Jobs API, Databricks CLI, Cron expressions


NEW QUESTION # 74
Which of the following is hosted completely in the control plane of the classic Databricks architecture?

  • A. Driver node
  • B. Databricks web application
  • C. Worker node
  • D. Databricks Filesystem
  • E. JDBC data source

Answer: B

Explanation:
1: The Databricks web application is the user interface that allows you to create and manage workspaces, clusters, notebooks, jobs, and other resources. It is hosted completely in the control plane of the classic Databricks architecture, which includes the backend services that Databricks manages in your Databricks account. The other options are part of the compute plane, which is where your data is processed by compute resources such as clusters. The compute plane is in your own cloud account and network. Reference: Databricks architecture overview, Security and Trust Center


NEW QUESTION # 75
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