You are validating the test results for the data migration strategy.
Please select two actions you should do for the validation.
Click on the arrows to vote for the correct answer
A. B. C. D. E.Correct Answers: B and.
D.
To successfully evaluate the data migration test results, a Solution Architect needs to define the data validation parameters.
These parameters include data quality and data quantity.
After you map the data for the transfer, it is necessary to verify the correctness of the data values.
Suppose you have a simple one-to-one field mapping or a complicated mapping that involves data transformation.
In that case, you need to establish procedures or implement tools that would verify that data migration produces the expected data output.
The data quality is a parameter that reflects the data value correctness due to the data mapping.
A Solution Architect needs to work with stakeholders to verify the data quality.
The data migration might produce a different number of output rows.
For example, because of data transformation or changed schema, or some other factors, the number of input rows can differ from the number of rows in a new database.
The data quantity is a parameter that reflects the expected number of rows migrated to the Dataverse.
Data quality and data quantity are essential values for data migration control and validation of the data migration strategy.
Both these parameters are vital for testing strategy as well.
Your test team needs to know the acceptance criteria for the data migration.
All other options are incorrect.
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When validating the test results for the data migration strategy, there are several actions you can take to ensure that the data has been migrated correctly. Out of the options provided, the two actions you should do for the validation are:
B. You need to establish parameters for data value correctness: When migrating data, it is crucial to ensure that the data is accurate and complete. To validate the test results, you should establish parameters for data value correctness. This means verifying that the data that was migrated is the same as the original data source. You can do this by comparing the data before and after migration or by using data profiling tools to check the integrity of the data. You should also test the data to ensure that it meets the requirements and specifications of the system.
D. You need to define the expected number of output rows: When migrating data, you should have an expected number of output rows in mind. This will help you to determine if the migration was successful and if there were any issues during the migration. You can use data profiling tools to count the number of rows before and after migration and ensure that the number of rows is the same. This is also helpful when troubleshooting any issues that may arise during the migration process.
A, C, and E are not actions you should do when validating the test results for the data migration strategy. Here's why:
A. You need to define the data connector: Defining the data connector is important when setting up the data migration strategy, but it is not an action you should take when validating the test results. The data connector is used to connect to the data source, but it does not verify the accuracy of the data.
C. You need to define the data loss prevention policy: Defining the data loss prevention policy is important when setting up the data migration strategy, but it is not an action you should take when validating the test results. The data loss prevention policy is used to prevent data loss during the migration process, but it does not verify the accuracy of the data.
E. You need to define the data transfer speed: Defining the data transfer speed is important when setting up the data migration strategy, but it is not an action you should take when validating the test results. The data transfer speed is used to optimize the migration process, but it does not verify the accuracy of the data.