1. [Marks: 5] Create a resource group in your Azure portal and deploy three resources.
Azure Data Factory, Azure SQL DB and Blob storage account.
2. [Marks: 15] Now create a pipeline in Azure Data Factory and copy
gender_jobs_data.csv file from the Blob storage account to Azure SQL DB. (First copy
this file from your local machine to Blob Storage). See this
3. [Marks: 10] Explain the different types of triggers available in ADF. Now create a
schedule trigger and run your pipeline every 3 minutes. Show 5 successful runs.
4. [Marks: 20] A client needs to replicate objects from ADLS Gen 2 in Canada Central
to ADLS Gen 2 in West Europe. Let’s say they want to do this in a bi-directional
way. How can you set this up?
[Hint: This probably can be done using Azure Data Factory and Event Triggers. For
eg; every time there is a new Blob on one side, it needs to be replicated on the other
In this part, you will use Query Editor in Azure SQL DB and use the gender_jobs_data.csv table
to perform the below queries.
For part B implementation, use the same table that is provided to you.
You need to use Azure SQL Database for this part.
1. [Marks:5] In the gender_jobs_data table – Filter all the OCCUPATIONS in
MAJOR_CATEGORY of Computer, Engineering, and Science for the YEAR 2013
2. [Marks:5] In the gender_jobs_data table – How many OCCUPATIONS exist in the
MINOR_CATEGORY of Business and Financial Operations overall?
3. [Marks:5] In the gender_jobs_data table – Get all relevant information for bus drivers
across all years
4. [Marks:5] In the gender_jobs_data table – Summarize the total number of
WORKERS_FEMALE in the MAJOR_CATEGORY of Management, Business, and
Financial by each year.
5. [Marks:5] In the gender_jobs_data table – What were the total earnings of male
(TOTAL_EARNINGS_MALE) employees in the Service MAJOR_CATEGORY for the
6. [Marks:5] In the gender_jobs_data table – How many female workers were in management
roles in the year 2015?
7. [Marks:5] In the gender_jobs_data table – Compare the TOTAL_EARNINGS_MALE and
TOTAL_EARNINGS_FEMALE earnings irrespective of occupation by each year
8. [Marks:5] In the gender_jobs_data table – How much money
(TOTAL_EARNINGS_FEMALE) did female workers make as engineers in 2016?
9. [Marks:10] What is the total number of full-time and part-time female workers versus male
workers year over year?