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You have an Azure subscription that contains a Microsoft Purview account named MP1, an Azure data factory named DF1, and a storage account named storage. MP1 is configured
10 scan storage1. DF1 is connected to MP1 and contains 3 dataset named DS1. DS1 references 2 file in storage.
In DF1, you plan to create a pipeline that will process data from DS1.
You need to review the schema and lineage information in MP1 for the data referenced by DS1.
Which two features can you use to locate the information? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
The two features that can be used to locate the schema and lineage information for the data referenced by DS1 are the search bar in Azure Data Factory Studio and the search bar in the Microsoft Purview governance portal.
The search bar in Azure Data Factory Studio allows you to search for the dataset DS1 and view its properties and lineage. This can help you locate information about the source and destination data stores, as well as the transformations that were applied to the data.
The search bar in the Microsoft Purview governance portal allows you to search for the storage account and view its metadata, including schema and lineage information. This can help you understand the different data assets that are stored in the storage account and how they are related to each other.
The Storage browser of storage1 in the Azure portal may allow you to view the files that are stored in the storage account, but it does not provide lineage or schema information for those files. Similarly, the search bar in the Azure portal may allow you to search for resources in the Azure subscription, but it does not provide detailed information about the data assets themselves.
You have two Azure Data Factory instances named ADFdev and ADFprod. ADFdev connects to an Azure DevOps Git repository.
You publish changes from the main branch of the Git repository to ADFdev.
You need to deploy the artifacts from ADFdev to ADFprod.
What should you do first?
In Azure Data Factory, continuous integration and delivery (CI/CD) means moving Data Factory pipelines from one environment (development, test, production) to another.
Note:
The following is a guide for setting up an Azure Pipelines release that automates the deployment of a data factory to multiple environments.
In Azure DevOps, open the project that's configured with your data factory.
On the left side of the page, select Pipelines, and then select Releases.
Select New pipeline, or, if you have existing pipelines, select New and then New release pipeline.
In the Stage name box, enter the name of your environment.
Select Add artifact, and then select the git repository configured with your development data factory. Select the publish branch of the repository for the Default branch. By default, this publish branch is adf_publish.
Select the Empty job template.
https://docs.microsoft.com/en-us/azure/data-factory/continuous-integration-deployment
You have an enterprise data warehouse in Azure Synapse Analytics named DW1 on a server named Server1.
You need to verify whether the size of the transaction log file for each distribution of DW1 is smaller than 160 GB.
What should you do?
The following query returns the transaction log size on each distribution. If one of the log files is reaching 160 GB, you should consider scaling up your instance or limiting your transaction size.
-- Transaction log size
SELECT
instance_name as distribution_db,
cntr_value*1.0/1048576 as log_file_size_used_GB,
pdw_node_id
FROM sys.dm_pdw_nodes_os_performance_counters
WHERE
instance_name like 'Distribution_%'
AND counter_name = 'Log File(s) Used Size (KB)'
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-manage-monitor
You have two Azure Blob Storage accounts named account1 and account2?
You plan to create an Azure Data Factory pipeline that will use scheduled intervals to replicate newly created or modified blobs from account1 to account?
You need to recommend a solution to implement the pipeline. The solution must meet the following requirements:
* Ensure that the pipeline only copies blobs that were created of modified since the most recent replication event.
* Minimize the effort to create the pipeline.
What should you recommend?
You plan to perform batch processing in Azure Databricks once daily.
Which type of Databricks cluster should you use?
Azure Databricks has two types of clusters: interactive and automated. You use interactive clusters to analyze data collaboratively with interactive notebooks. You use automated clusters to run fast and robust automated jobs.
Example: Scheduled batch workloads (data engineers running ETL jobs)
This scenario involves running batch job JARs and notebooks on a regular cadence through the Databricks platform.
The suggested best practice is to launch a new cluster for each run of critical jobs. This helps avoid any issues (failures, missing SLA, and so on) due to an existing workload (noisy neighbor) on a shared cluster.
