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Certification Databricks-Certified-Data-Engineer-Associate Book Torrent | Simulated Databricks-Certified-Data-Engineer-Associate Test

Certification Databricks-Certified-Data-Engineer-Associate Book Torrent | Simulated Databricks-Certified-Data-Engineer-Associate Test

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q83-Q88):

NEW QUESTION # 83
A Delta Live Table pipeline includes two datasets defined using streaming live table. Three datasets are defined against Delta Lake table sources using live table.
The table is configured to run in Production mode using the Continuous Pipeline Mode.
What is the expected outcome after clicking Start to update the pipeline assuming previously unprocessed data exists and all definitions are valid?

  • A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped.
  • B. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
  • C. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
  • D. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.

Answer: A

Explanation:
In Delta Live Tables (DLT), when configured to run in Continuous Pipeline Mode, particularly in a production environment, the system is designed to continuously process and update data as it becomes available. This mode keeps the compute resources active to handle ongoing data processing and automatically updates all datasets defined in the pipeline at predefined intervals. Once the pipeline is manually stopped, the compute resources are terminated to conserve resources and reduce costs. This mode is suitable for production environments where datasets need to be kept up-to-date with the latest data.
Reference:
Databricks documentation on Delta Live Tables: Delta Live Tables Guide


NEW QUESTION # 84
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 the query's refresh schedule to end on a certain date in the query scheduler.
  • D. They can set a limit to the number of individuals that are able to manage the query's refresh schedule.
  • E. They can set the query's refresh schedule to end after a certain number of refreshes.

Answer: C

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 # 85
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 clusters that are from a cluster pool
  • C. They can configure the clusters to autoscale for larger data sizes
  • D. They can use endpoints available in Databricks SQL
  • E. They can use jobs clusters instead of all-purpose clusters

Answer: B

Explanation:
The best action that the data engineer can perform to improve the start up time for the clusters used for the Job is to use clusters that are from a cluster pool. A cluster pool is a set of idle clusters that can be used by jobs or interactive sessions. By using a cluster pool, the data engineer can avoid the cluster creation time and reduce the latency of the tasks. Cluster pools also offer cost savings and resource efficiency, as they can be shared by multiple users and jobs.
Option A is not relevant, as endpoints available in Databricks SQL are used for creating and managing SQL analytics workloads, not for improving cluster start up time.
Option B is not correct, as jobs clusters and all-purpose clusters have similar start up times. Jobs clusters are clusters that are dedicated to run a single job and are terminated when the job is completed. All-purpose clusters are clusters that can be used for multiple purposes, such as interactive sessions, notebooks, or multiple jobs. Both types of clusters can benefit from using a cluster pool.
Option C is not advisable, as configuring the clusters to be single-node will reduce the parallelism and performance of the tasks. Single-node clusters are clusters that have only one worker node and are typically used for testing or development purposes. They are not suitable for running production jobs that require high scalability and fault tolerance.
Option E is not helpful, as configuring the clusters to autoscale for larger data sizes will not affect the start up time of the clusters. Autoscaling is a feature that allows clusters to dynamically adjust the number of worker nodes based on the workload. It can help optimize the resource utilization and cost efficiency of the clusters, but it does not speed up the cluster creation process.
Reference:
Cluster Pools
Jobs
Clusters
[Databricks Data Engineer Professional Exam Guide]


NEW QUESTION # 86
A Delta Live Table pipeline includes two datasets defined using STREAMING LIVE TABLE. Three datasets are defined against Delta Lake table sources using LIVE TABLE.
The table is configured to run in Development mode using the Continuous Pipeline Mode.
Assuming previously unprocessed data exists and all definitions are valid, what is the expected outcome after clicking Start to update the pipeline?

  • A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist to allow for additional testing.
  • B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist until the pipeline is shut down.
  • C. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional testing.
  • D. All datasets will be updated once and the pipeline will persist without any processing. The compute resources will persist but go unused.
  • E. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated.

Answer: A

Explanation:
Explanation
You can optimize pipeline execution by switching between development and production modes. Use the Delta Live Tables Environment Toggle Icon buttons in the Pipelines UI to switch between these two modes. By default, pipelines run in development mode.
When you run your pipeline in development mode, the Delta Live Tables system does the following:
Reuses a cluster to avoid the overhead of restarts. By default, clusters run for two hours when development mode is enabled. You can change this with the pipelines.clusterShutdown.delay setting in the Configure your compute settings.
Disables pipeline retries so you can immediately detect and fix errors.
In production mode, the Delta Live Tables system does the following:
Restarts the cluster for specific recoverable errors, including memory leaks and stale credentials.
Retries execution in the event of specific errors, for example, a failure to start a cluster.
https://docs.databricks.com/en/delta-live-tables/updates.html#optimize-execution


NEW QUESTION # 87
A data engineer wants to schedule their Databricks SQL dashboard to refresh once per day, but they only want the associated SQL endpoint to be running when it is necessary.
Which of the following approaches can the data engineer use to minimize the total running time of the SQL endpoint used in the refresh schedule of their dashboard?

  • A. They can reduce the cluster size of the SQL endpoint.
  • B. They can ensure the dashboard's SQL endpoint is not one of the included query's SQL endpoint.
  • C. They can set up the dashboard's SQL endpoint to be serverless.
  • D. They can ensure the dashboard's SQL endpoint matches each of the queries' SQL endpoints.
  • E. They can turn on the Auto Stop feature for the SQL endpoint.

Answer: C

Explanation:
A serverless SQL endpoint is a compute resource that is automatically managed by Databricks and scales up or down based on the workload. A serverless SQL endpoint can be used to run queries and dashboards without requiring manual configuration or management. A serverless SQL endpoint is only active when it is needed and shuts down automatically when idle, minimizing the total running time and cost. A serverless SQL endpoint can be created and assigned to a dashboard using the Databricks SQL UI or the SQL Analytics API. Reference:
Create a serverless SQL endpoint
Assign a SQL endpoint to a dashboard
SQL Analytics API


NEW QUESTION # 88
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