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Databricks-Certified-Data-Engineer-Associate Valid Test Question | Practice Databricks-Certified-Data-Engineer-Associate Engine

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q71-Q76):

NEW QUESTION # 71
A data engineer has a single-task Job that runs each morning before they begin working. After identifying an upstream data issue, they need to set up another task to run a new notebook prior to the original task.
Which of the following approaches can the data engineer use to set up the new task?

A. They can create a new job from scratch and add both tasks to run concurrently.
B. They can clone the existing task to a new Job and then edit it to run the new notebook.
C. They can create a new task in the existing Job and then add it as a dependency of the original task.
D. They can create a new task in the existing Job and then add the original task as a dependency of the new task.
E. They can clone the existing task in the existing Job and update it to run the new notebook.

Answer: C

Explanation:
Explanation
To set up the new task to run a new notebook prior to the original task in a single-task Job, the data engineer can use the following approach: In the existing Job, create a new task that corresponds to the new notebook that needs to be run. Set up the new task with the appropriate configuration, specifying the notebook to be executed and any necessary parameters or dependencies. Once the new task is created, designate it as a dependency of the original task in the Job configuration. This ensures that the new task is executed before the original task.

NEW QUESTION # 72
An engineering manager uses a Databricks SQL query to monitor ingestion latency for each data source. The manager checks the results of the query every day, but they are manually rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are updated each day?

A. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL.
B. They can schedule the query to run every 12 hours from the Jobs UI.
C. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL.
D. They can schedule the query to run every 1 day from the Jobs UI.
E. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL.

Answer: A

Explanation:
Databricks SQL allows users to schedule queries to run automatically at a specified frequency and time zone.
This can help users to keep their dashboards or alerts updated with the latest data. To schedule a query, users need to do the following steps:
* In the Query Editor, click Schedule > Add schedule to open a menu with schedule settings.
* Choose when to run the query. Use the dropdown pickers to specify the frequency, period, starting time, and time zone. Optionally, select the Show cron syntax checkbox to edit the schedule in Quartz Cron Syntax.
* Choose More options to show optional settings. Users can also choose a name for the schedule, and a SQL warehouse to power the query.
* Click Create. The query will run automatically according to the schedule.
The other options are incorrect because they do not refer to the correct location or frequency to schedule the query. The query's page in Databricks SQL is the place where users can edit, run, or schedule the query. The SQL endpoint's page in Databricks SQL is the place where users can manage the SQL warehouses and SQL endpoints. The Jobs UI is the place where users can create, run, or schedule jobs that execute notebooks, JARs, or Python scripts. References: Schedule a query, What are Databricks SQL alerts?, Jobs.

NEW QUESTION # 73
Which of the following can be used to simplify and unify siloed data architectures that are specialized for specific use cases?

A. None of these
B. Data warehouse
C. Data lakehouse
D. Data lake
E. All of these

Answer: C

Explanation:
A data lakehouse is a new paradigm that can be used to simplify and unify siloed data architectures that are specialized for specific use cases. A data lakehouse combines the best of both data lakes and data warehouses, providing a single platform that supports diverse data types, open standards, low-cost storage, high-performance queries, ACID transactions, schema enforcement, and governance. A data lakehouse enables data engineers to build reliable and scalable data pipelines that can serve various downstream applications and users, such as data science, machine learning, analytics, and reporting. A data lakehouse leverages the power of Delta Lake, a storage layer that brings reliability and performance to data lakes. References: What is a data lakehouse?, Delta Lake, Lakehouse: A New Generation of Open Platforms that Unify Data Warehousing and Advanced Analytics

NEW QUESTION # 74
A data engineer needs to use a Delta table as part of a data pipeline, but they do not know if they have the appropriate permissions.
In which of the following locations can the data engineer review their permissions on the table?

A. Jobs
B. Databricks Filesystem
C. Repos
D. Dashboards
E. Data Explorer

Answer: E

Explanation:
Data Explorer is a graphical interface that allows you to browse, create, and manage data objects such as databases, tables, and views in your workspace. You can also review and modify the permissions on these data objects using Data Explorer. To access Data Explorer, you can click on the Data icon in the sidebar, or use the
%sql magic command in a notebook. You can then select a database and a table, and click on the Permissions tab to view and edit the access control lists (ACLs) for the table. You can also use SQL commands such as SHOW GRANT and GRANT to query and modify the permissions on a Delta table. References:
* Data Explorer
* Access control for Delta tables
* SHOW GRANT
* [GRANT]

NEW QUESTION # 75
Which query is performing a streaming hop from raw data to a Bronze table?

Answer: A

NEW QUESTION # 76
......

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