You are creating an Azure Data Factory data flow that will ingest data from a CSV file, cast columns to specified types of data, and insert the data into a table in an Azure Synapse Analytic dedicated SQL pool. The CSV file contains three columns named username, comment, and date.
The data flow already contains the following:
A source transformation.
A Derived Column transformation to set the appropriate types of data.
A sink transformation to land the data in the pool.
You need to ensure that the data flow meets the following requirements:
All valid rows must be written to the destination table.
Truncation errors in the comment column must be avoided proactively.
Any rows containing comment values that will cause truncation errors upon insert must be written to a file in blob storage.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

• A.To the data flow, add a sink transformation to write the rows to a file in blob storage.
• B.To the data flow, add a Conditional Split transformation to separate the rows that will cause truncation errors.
• C.To the data flow, add a filter transformation to filter out rows that will cause truncation errors.
• D.Add a select transformation to select only the rows that will cause truncation errors.

Correct Answer: A,B

Explanation
B: Example:
1. This conditional split transformation defines the maximum length of "title" to be five. Any row that is less than or equal to five will go into the GoodRows stream. Any row that is larger than five will go into the BadRows stream.

2. This conditional split transformation defines the maximum length of "title" to be five. Any row that is less than or equal to five will go into the GoodRows stream. Any row that is larger than five will go into the BadRows stream.
A:
3. Now we need to log the rows that failed. Add a sink transformation to the BadRows stream for logging.
Here, we'll "auto-map" all of the fields so that we have logging of the complete transaction record. This is a text-delimited CSV file output to a single file in Blob Storage. We'll call the log file "badrows.csv".

4. The completed data flow is shown below. We are now able to split off error rows to avoid the SQL truncation errors and put those entries into a log file. Meanwhile, successful rows can continue to write to our target database.

Reference:
https://docs.microsoft.com/en-us/azure/data-factory/how-to-data-flow-error-rows

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You are designing the folder structure for an Azure Data Lake Storage Gen2 account.
You identify the following usage patterns:
* Users will query data by using Azure Synapse Analytics serverless SQL pools and Azure Synapse Analytics serverless Apache Spark pods.
* Most queries will include a filter on the current year or week.
* Data will be secured by data source.
You need to recommend a folder structure that meets the following requirements:
* Supports the usage patterns
* Simplifies folder security
* Minimizes query times
Which folder structure should you recommend?
A)

B)

C)

D)

E)

• A.Option A
• B.Option B
• C.Option C
• D.Option D
• E.Option E

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You have an Azure data factory.
You need to ensure that pipeline-run data is retained for 120 days. The solution must ensure that you can query the data by using the Kusto query language.
Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

Correct Answer:

Explanation:

Step 1: Create an Azure Storage account that has a lifecycle policy
To automate common data management tasks, Microsoft created a solution based on Azure Data Factory. The service, Data Lifecycle Management, makes frequently accessed data available and archives or purges other data according to retention policies. Teams across the company use the service to reduce storage costs, improve app performance, and comply with data retention policies.
Step 2: Create a Log Analytics workspace that has Data Retention set to 120 days.
Data Factory stores pipeline-run data for only 45 days. Use Azure Monitor if you want to keep that data for a longer time. With Monitor, you can route diagnostic logs for analysis to multiple different targets, such as a Storage Account: Save your diagnostic logs to a storage account for auditing or manual inspection. You can use the diagnostic settings to specify the retention time in days.
Step 3: From Azure Portal, add a diagnostic setting.
Step 4: Send the data to a log Analytics workspace,
Event Hub: A pipeline that transfers events from services to Azure Data Explorer.
Keeping Azure Data Factory metrics and pipeline-run data.
Configure diagnostic settings and workspace.
Create or add diagnostic settings for your data factory.
In the portal, go to Monitor. Select Settings > Diagnostic settings.
Select the data factory for which you want to set a diagnostic setting.
If no settings exist on the selected data factory, you're prompted to create a setting. Select Turn on diagnostics.
Give your setting a name, select Send to Log Analytics, and then select a workspace from Log Analytics Workspace.
Select Save.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/monitor-using-azure-monitor

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You need to design a data retention solution for the Twitter feed data records. The solution must meet the customer sentiment analytics requirements.
Which Azure Storage functionality should you include in the solution?

• A.change feed
• B.soft delete
• C.time-based retention
• D.lifecycle management

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You are designing a folder structure for the files m an Azure Data Lake Storage Gen2 account. The account has one container that contains three years of data.
You need to recommend a folder structure that meets the following requirements:
* Supports partition elimination for queries by Azure Synapse Analytics serverless SQL pooh
* Supports fast data retrieval for data from the current month
* Simplifies data security management by department
Which folder structure should you recommend?

• A.\DataSource\Department\YYYYMM\DataFile_YYYYMMDD.parquet
• B.\Depdftment\DataSource\YYY\MM\DataFile_YYYYMMDD.parquet
• C.\YYY\MM\DD\Department\DataSource\DataFile_YYYMMMDD.parquet
• D.\DD\MM\YYYY\Department\DataSource\DataFile_DDMMYY.parquet

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