- (Exam Topic 3)
You have an Azure Data Factory pipeline named Pipeline1!. Pipelinel contains a copy activity that sends data to an Azure Data Lake Storage Gen2 account. Pipeline 1 is executed by a schedule trigger.
You change the copy activity sink to a new storage account and merge the changes into the collaboration branch.
After Pipelinel executes, you discover that data is NOT copied to the new storage account. You need to ensure that the data is copied to the new storage account.
What should you do?
Correct Answer:
A
CI/CD lifecycle
A development data factory is created and configured with Azure Repos Git. All developers should have permission to author Data Factory resources like pipelines and datasets. A developer creates a feature branch to make a change. They debug their pipeline runs with their most recent changes After a developer is satisfied with their changes, they create a pull request from their feature branch to the main or collaboration branch to get their changes reviewed by peers. After a pull request is approved and changes are merged in the main branch, the changes get published to the development factory.
Reference: https://docs.microsoft.com/en-us/azure/data-factory/continuous-integration-delivery
- (Exam Topic 1)
You need to implement an Azure Synapse Analytics database object for storing the sales transactions data. The solution must meet the sales transaction dataset requirements.
What solution must meet the sales transaction dataset requirements.
What should you do? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Solution:
Graphical user interface, text, application, table Description automatically generated
Box 1: Create table
Scenario: Load the sales transaction dataset to Azure Synapse Analytics Box 2: RANGE RIGHT FOR VALUES
Scenario: Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right.
RANGE RIGHT: Specifies the boundary value belongs to the partition on the right (higher values). FOR VALUES ( boundary_value [,...n] ): Specifies the boundary values for the partition.
Scenario: Load the sales transaction dataset to Azure Synapse Analytics. Contoso identifies the following requirements for the sales transaction dataset:
Partition data that contains sales transaction records. Partitions must be designed to provide efficient loads by month. Boundary values must belong to the partition on the right. Ensure that queries joining and filtering sales transaction records based on product ID complete as quickly as possible. Implement a surrogate key to account for changes to the retail store addresses. Ensure that data storage costs and performance are predictable. Minimize how long it takes to remove old records. Reference:
https://docs.microsoft.com/en-us/sql/t-sql/statements/create-table-azure-sql-data-warehouse
Does this meet the goal?
Correct Answer:
A
- (Exam Topic 3)
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this scenario, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have an Azure Storage account that contains 100 GB of files. The files contain text and numerical values. 75% of the rows contain description data that has an average length of 1.1 MB.
You plan to copy the data from the storage account to an Azure SQL data warehouse. You need to prepare the files to ensure that the data copies quickly.
Solution: You modify the files to ensure that each row is less than 1 MB.
Does this meet the goal?
Correct Answer:
A
When exporting data into an ORC File Format, you might get Java out-of-memory errors when there are large text columns. To work around this limitation, export only a subset of the columns.
References:
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/guidance-for-loading-data
- (Exam Topic 3)
You have an Azure Synapse Analytics dedicated SQL pool.
You need to create a table named FactInternetSales that will be a large fact table in a dimensional model. FactInternetSales will contain 100 million rows and two columns named SalesAmount and OrderQuantity. Queries executed on FactInternetSales will aggregate the values in SalesAmount and OrderQuantity from the last year for a specific product. The solution must minimize the data size and query execution time.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Solution:
Box 1: (CLUSTERED COLUMNSTORE INDEX CLUSTERED COLUMNSTORE INDEX
Columnstore indexes are the standard for storing and querying large data warehousing fact tables. This index uses column-based data storage and query processing to achieve gains up to 10 times the query performance in your data warehouse over traditional row-oriented storage. You can also achieve gains up to 10 times the data
compression over the uncompressed data size. Beginning with SQL Server 2016 (13.x) SP1, columnstore indexes enable operational analytics: the ability to run performant real-time analytics on a transactional workload.
Note: Clustered columnstore index
A clustered columnstore index is the physical storage for the entire table. Diagram Description automatically generated
To reduce fragmentation of the column segments and improve performance, the columnstore index might store some data temporarily into a clustered index called a deltastore and a B-tree list of IDs for deleted rows. The deltastore operations are handled behind the scenes. To return the correct query results, the clustered columnstore index combines query results from both the columnstore and the deltastore.
Box 2: HASH([ProductKey])
A hash distributed table distributes rows based on the value in the distribution column. A hash distributed table is designed to achieve high performance for queries on large tables.
Choose a distribution column with data that distributes evenly
Reference: https://docs.microsoft.com/en-us/sql/relational-databases/indexes/columnstore-indexes-overview https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-overvie https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-tables-distribu
Does this meet the goal?
Correct Answer:
A
- (Exam Topic 3)
You have a partitioned table in an Azure Synapse Analytics dedicated SQL pool. You need to design queries to maximize the benefits of partition elimination. What should you include in the Transact-SQL queries?
Correct Answer:
B
