DP-600: Implementing Analytics Solutions Using Microsoft Fabric
Overview
Contoso, Ltd. is a US-based health supplements company. Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sales and Retail Sales. The Research division assigns internally developed product lines to individual teams of researchers and analysts.
Existing Environment
Identity Environment
Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroup1 and ResearchReviewersGroup2.
Data Environment
Contoso has the following data environment:
- The Sales division uses a Microsoft Power BI Premium capacity.
- The semantic model of the Online Sales department includes a fact table named Orders that uses Import made. In the system of origin, the OrderID value represents the sequence in which orders are created.
- The Research department uses an on-premises, third-party data warehousing product.
- Fabric is enabled for contoso.com.
- An Azure Data Lake Storage Gen2 storage account named storage1 contains Research division data for a product line named Productline1. The data is in the delta format.
- A Data Lake Storage Gen2 storage account named storage2 contains Research division data for a product line named Productline2. The data is in the CSV format.
Requirements
Planned Changes
Contoso plans to make the following changes:
- Enable support for Fabric in the Power BI Premium capacity used by the Sales division.
- Make all the data for the Sales division and the Research division available in Fabric.
- For the Research division, create two Fabric workspaces named Productline1ws and Productine2ws.
- In Productline1ws, create a lakehouse named Lakehouse1.
- In Lakehouse1, create a shortcut to storage1 named ResearchProduct.
Data Analytics Requirements
Contoso identifies the following data analytics requirements:
- All the workspaces for the Sales division and the Research division must support all Fabric experiences.
- The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing.
- The Research division workspaces must be grouped together logically to support OneLake data hub filtering based on the department name.
- For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints.
- For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer.
- All the semantic models and reports for the Research division must use version control that supports branching.
Data Preparation Requirements
Contoso identifies the following data preparation requirements:
- The Research division data for Productline1 must be retrieved from Lakehouse1 by using Fabric notebooks.
- All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.
Semantic Model Requirements
Contoso identifies the following requirements for implementing and managing semantic models:
- The number of rows added to the Orders table during refreshes must be minimized.
- The semantic models in the Research division workspaces must use Direct Lake mode.
General Requirements
Contoso identifies the following high-level requirements that must be considered for all solutions:
- Follow the principle of least privilege when applicable.
- Minimize implementation and maintenance effort when possible.
Which syntax should you use in a notebook to access the Research division data for Productline1?
spark.read.format(“delta”).load(“Tables/productline1/ResearchProduct”)
spark.sql(“SELECT * FROM Lakehouse1.ResearchProduct ”)
external_table(‘Tables/ResearchProduct)
external_table(ResearchProduct)
Answer is spark.sql(“SELECT * FROM Lakehouse1.ResearchProduct ”)
For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints.
df = spark.read.format("delta").load("Tables/MyShortcut")
display(df)
OR
df = spark.sql("SELECT * FROM MyLakehouse.MyShortcut LIMIT 1000")
display(df)
Reference:
https://learn.microsoft.com/en-us/fabric/onelake/onelake-shortcuts
Overview
Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.
Existing Environment
Fabric Environment
Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.
Available Data
Litware has data that must be analyzed as shown in the following table.
The Product data contains a single table and the following columns.
Response
For each survey submitted, the following occurs:
- One row is added to the Survey table.
- One row is added to the Response table for each question in the survey.
- The Question table contains the text of each survey question. The third question in each survey response is an overall satisfaction score. Customers can submit a survey after each purchase.
User Problems
The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.
Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.
Requirements
Planned Changes
Litware plans to enable Fabric features in the existing tenant. The analytics team will create a new data store as a proof of concept (PoC). The remaining Liware users will only get access to the Fabric features once the PoC is complete. The PoC will be completed by using a Fabric trial capacity
The following three workspaces will be created:
- AnalyticsPOC: Will contain the data store, semantic models, reports pipelines, dataflow, and notebooks used to populate the data store
- DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake
- DataSciPOC: Will contain all the notebooks and reports created by the data scientists
The following will be created in the AnalyticsPOC workspace:
- A data store (type to be decided)
A custom semantic model
A default semantic model
Interactive reports
The data engineers will create data pipelines to load data to OneLake either hourly or daily depending on the data source. The analytics engineers will create processes to ingest, transform, and load the data to the data store in the AnalyticsPOC workspace daily. Whenever possible, the data engineers will use low-code tools for data ingestion. The choice of which data cleansing and transformation tools to use will be at the data engineers’ discretion.
All the semantic models and reports in the Analytics POC workspace will use the data store as the sole data source.
Technical Requirements
The data store must support the following:
- Read access by using T-SQL or Python
- Semi-structured and unstructured data
- Row-level security (RLS) for users executing T-SQL queries
Files loaded by the data engineers to OneLake will be stored in the Parquet format and will meet Delta Lake specifications.
Data will be loaded without transformation in one area of the AnalyticsPOC data store. The data will then be cleansed, merged, and transformed into a dimensional model The data load process must ensure that the raw and cleansed data is updated completely before populating the dimensional model.
The dimensional model must contain a date dimension. There is no existing data source for the date dimension. The Litware fiscal year matches the calendar year. The date dimension must always contain dates from 2010 through the end of the current year.
The product pricing group logic must be maintained by the analytics engineers in a single location. The pricing group data must be made available in the data store for T-SOL. queries and in the default semantic model. The following logic must be used:
- List prices that are less than or equal to 50 are in the low pricing group.
- List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
- List prices that are greater than 1,000 are in the high pricing group.
Security Requirements
Only Fabric administrators and the analytics team must be able to see the Fabric items created as part of the PoC.
Litware identifies the following security requirements for the Fabric items in the AnalyticsPOC workspace:
- Fabric administrators will be the workspace administrators.
- The data engineers must be able to read from and write to the data store. No access must be granted to datasets or reports.
- The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
- The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook
- The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.
- The date dimension must be available to all users of the data store.
- The principle of least privilege must be followed.
Both the default and custom semantic models must include only tables or views from the dimensional model in the data store. Litware already has the following Microsoft Entra security groups:
- FabricAdmins: Fabric administrators
- AnalyticsTeam: All the members of the analytics team
- DataAnalysts: The data analysts on the analytics team
- DataScientists: The data scientists on the analytics team
- DataEngineers: The data engineers on the analytics team
- AnalyticsEngineers: The analytics engineers on the analytics team
Report Requirements
The data analysts must create a customer satisfaction report that meets the following requirements:
- Enables a user to select a product to filter customer survey responses to only those who have purchased that product.
- Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected dat.
- Shows data as soon as the data is updated in the data store.
- Ensures that the report and the semantic model only contain data from the current and previous year.
- Ensures that the report respects any table-level security specified in the source data store.
- Minimizes the execution time of report queries.
You need to assign permissions for the data store in the AnalyticsPOC workspace. The solution must meet the security requirements.
Which additional permissions should you assign when you share the data store?
Check the answer section
Data Engineers = Read sql endpoints
Data Analyst = Build Reports
Data Scientist = They prefer Spark Always
Reference:
https://blog.fabric.microsoft.com/en-US/blog/lakehouse-sharing-and-access-permission-management/
Overview
Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.
Existing Environment
Fabric Environment
Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.
Available Data
Litware has data that must be analyzed as shown in the following table.
The Product data contains a single table and the following columns.
Response
For each survey submitted, the following occurs:
- One row is added to the Survey table.
- One row is added to the Response table for each question in the survey.
- The Question table contains the text of each survey question. The third question in each survey response is an overall satisfaction score. Customers can submit a survey after each purchase.
User Problems
The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.
Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.
Requirements
Planned Changes
Litware plans to enable Fabric features in the existing tenant. The analytics team will create a new data store as a proof of concept (PoC). The remaining Liware users will only get access to the Fabric features once the PoC is complete. The PoC will be completed by using a Fabric trial capacity
The following three workspaces will be created:
- AnalyticsPOC: Will contain the data store, semantic models, reports pipelines, dataflow, and notebooks used to populate the data store
- DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake
- DataSciPOC: Will contain all the notebooks and reports created by the data scientists
The following will be created in the AnalyticsPOC workspace:
- A data store (type to be decided)
A custom semantic model
A default semantic model
Interactive reports
The data engineers will create data pipelines to load data to OneLake either hourly or daily depending on the data source. The analytics engineers will create processes to ingest, transform, and load the data to the data store in the AnalyticsPOC workspace daily. Whenever possible, the data engineers will use low-code tools for data ingestion. The choice of which data cleansing and transformation tools to use will be at the data engineers’ discretion.
All the semantic models and reports in the Analytics POC workspace will use the data store as the sole data source.
Technical Requirements
The data store must support the following:
- Read access by using T-SQL or Python
- Semi-structured and unstructured data
- Row-level security (RLS) for users executing T-SQL queries
Files loaded by the data engineers to OneLake will be stored in the Parquet format and will meet Delta Lake specifications.
Data will be loaded without transformation in one area of the AnalyticsPOC data store. The data will then be cleansed, merged, and transformed into a dimensional model The data load process must ensure that the raw and cleansed data is updated completely before populating the dimensional model.
The dimensional model must contain a date dimension. There is no existing data source for the date dimension. The Litware fiscal year matches the calendar year. The date dimension must always contain dates from 2010 through the end of the current year.
The product pricing group logic must be maintained by the analytics engineers in a single location. The pricing group data must be made available in the data store for T-SOL. queries and in the default semantic model. The following logic must be used:
- List prices that are less than or equal to 50 are in the low pricing group.
- List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
- List prices that are greater than 1,000 are in the high pricing group.
Security Requirements
Only Fabric administrators and the analytics team must be able to see the Fabric items created as part of the PoC.
Litware identifies the following security requirements for the Fabric items in the AnalyticsPOC workspace:
- Fabric administrators will be the workspace administrators.
- The data engineers must be able to read from and write to the data store. No access must be granted to datasets or reports.
- The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
- The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook
- The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.
- The date dimension must be available to all users of the data store.
- The principle of least privilege must be followed.
Both the default and custom semantic models must include only tables or views from the dimensional model in the data store. Litware already has the following Microsoft Entra security groups:
- FabricAdmins: Fabric administrators
- AnalyticsTeam: All the members of the analytics team
- DataAnalysts: The data analysts on the analytics team
- DataScientists: The data scientists on the analytics team
- DataEngineers: The data engineers on the analytics team
- AnalyticsEngineers: The analytics engineers on the analytics team
Report Requirements
The data analysts must create a customer satisfaction report that meets the following requirements:
- Enables a user to select a product to filter customer survey responses to only those who have purchased that product.
- Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected dat.
- Shows data as soon as the data is updated in the data store.
- Ensures that the report and the semantic model only contain data from the current and previous year.
- Ensures that the report respects any table-level security specified in the source data store.
- Minimizes the execution time of report queries.
You need to resolve the issue with the pricing group classification.
How should you complete the T-SQL statement?
Check the answer section
Answers are;
VIEW
CASE
WHEN(ListPrice > 50 AND ListPrice < 1000 THEN 'medium'
* VIEW: Shows data as soon as the data is updated in the data store.
* CASE: correct syntax before the WHENs.
* WHEN(ListPrice > 50 AND ListPrice < 1000 THEN 'medium': The second option will generate 2 rows for any '50' answer; the third option has the same problem plus a miss spelled ' ) ' that will end up generating a syntax error.
The only answer that will not generate a cartesian product is the first one, although it will miss all the '1000' answers. That's a smaller problem than a Cartesian product.
Overview
Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.
Existing Environment
Fabric Environment
Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.
Available Data
Litware has data that must be analyzed as shown in the following table.
The Product data contains a single table and the following columns.
Response
For each survey submitted, the following occurs:
- One row is added to the Survey table.
- One row is added to the Response table for each question in the survey.
- The Question table contains the text of each survey question. The third question in each survey response is an overall satisfaction score. Customers can submit a survey after each purchase.
User Problems
The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.
Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.
Requirements
Planned Changes
Litware plans to enable Fabric features in the existing tenant. The analytics team will create a new data store as a proof of concept (PoC). The remaining Liware users will only get access to the Fabric features once the PoC is complete. The PoC will be completed by using a Fabric trial capacity
The following three workspaces will be created:
- AnalyticsPOC: Will contain the data store, semantic models, reports pipelines, dataflow, and notebooks used to populate the data store
- DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake
- DataSciPOC: Will contain all the notebooks and reports created by the data scientists
The following will be created in the AnalyticsPOC workspace:
- A data store (type to be decided)
A custom semantic model
A default semantic model
Interactive reports
The data engineers will create data pipelines to load data to OneLake either hourly or daily depending on the data source. The analytics engineers will create processes to ingest, transform, and load the data to the data store in the AnalyticsPOC workspace daily. Whenever possible, the data engineers will use low-code tools for data ingestion. The choice of which data cleansing and transformation tools to use will be at the data engineers’ discretion.
All the semantic models and reports in the Analytics POC workspace will use the data store as the sole data source.
Technical Requirements
The data store must support the following:
- Read access by using T-SQL or Python
- Semi-structured and unstructured data
- Row-level security (RLS) for users executing T-SQL queries
Files loaded by the data engineers to OneLake will be stored in the Parquet format and will meet Delta Lake specifications.
Data will be loaded without transformation in one area of the AnalyticsPOC data store. The data will then be cleansed, merged, and transformed into a dimensional model The data load process must ensure that the raw and cleansed data is updated completely before populating the dimensional model.
The dimensional model must contain a date dimension. There is no existing data source for the date dimension. The Litware fiscal year matches the calendar year. The date dimension must always contain dates from 2010 through the end of the current year.
The product pricing group logic must be maintained by the analytics engineers in a single location. The pricing group data must be made available in the data store for T-SOL. queries and in the default semantic model. The following logic must be used:
- List prices that are less than or equal to 50 are in the low pricing group.
- List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
- List prices that are greater than 1,000 are in the high pricing group.
Security Requirements
Only Fabric administrators and the analytics team must be able to see the Fabric items created as part of the PoC.
Litware identifies the following security requirements for the Fabric items in the AnalyticsPOC workspace:
- Fabric administrators will be the workspace administrators.
- The data engineers must be able to read from and write to the data store. No access must be granted to datasets or reports.
- The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
- The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook
- The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.
- The date dimension must be available to all users of the data store.
- The principle of least privilege must be followed.
Both the default and custom semantic models must include only tables or views from the dimensional model in the data store. Litware already has the following Microsoft Entra security groups:
- FabricAdmins: Fabric administrators
- AnalyticsTeam: All the members of the analytics team
- DataAnalysts: The data analysts on the analytics team
- DataScientists: The data scientists on the analytics team
- DataEngineers: The data engineers on the analytics team
- AnalyticsEngineers: The analytics engineers on the analytics team
Report Requirements
The data analysts must create a customer satisfaction report that meets the following requirements:
- Enables a user to select a product to filter customer survey responses to only those who have purchased that product.
- Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected dat.
- Shows data as soon as the data is updated in the data store.
- Ensures that the report and the semantic model only contain data from the current and previous year.
- Ensures that the report respects any table-level security specified in the source data store.
- Minimizes the execution time of report queries.
What should you recommend using to ingest the customer data into the data store in the AnalyticsPOC workspace?
a stored procedure
a pipeline that contains a KQL activity
a Spark notebook
a dataflow
Answer is a dataflow
In the Interactive reports requirement, it stated, "Whenever possible, the data engineers will use low-code tools for data ingestion".
Ideally, It should be the COPY activity of the pipeline, but that is not given as a choice.
Overview
Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.
Existing Environment
Fabric Environment
Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.
Available Data
Litware has data that must be analyzed as shown in the following table.
The Product data contains a single table and the following columns.
Response
For each survey submitted, the following occurs:
- One row is added to the Survey table.
- One row is added to the Response table for each question in the survey.
- The Question table contains the text of each survey question. The third question in each survey response is an overall satisfaction score. Customers can submit a survey after each purchase.
User Problems
The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.
Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.
Requirements
Planned Changes
Litware plans to enable Fabric features in the existing tenant. The analytics team will create a new data store as a proof of concept (PoC). The remaining Liware users will only get access to the Fabric features once the PoC is complete. The PoC will be completed by using a Fabric trial capacity
The following three workspaces will be created:
- AnalyticsPOC: Will contain the data store, semantic models, reports pipelines, dataflow, and notebooks used to populate the data store
- DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake
- DataSciPOC: Will contain all the notebooks and reports created by the data scientists
The following will be created in the AnalyticsPOC workspace:
- A data store (type to be decided)
A custom semantic model
A default semantic model
Interactive reports
The data engineers will create data pipelines to load data to OneLake either hourly or daily depending on the data source. The analytics engineers will create processes to ingest, transform, and load the data to the data store in the AnalyticsPOC workspace daily. Whenever possible, the data engineers will use low-code tools for data ingestion. The choice of which data cleansing and transformation tools to use will be at the data engineers’ discretion.
All the semantic models and reports in the Analytics POC workspace will use the data store as the sole data source.
Technical Requirements
The data store must support the following:
- Read access by using T-SQL or Python
- Semi-structured and unstructured data
- Row-level security (RLS) for users executing T-SQL queries
Files loaded by the data engineers to OneLake will be stored in the Parquet format and will meet Delta Lake specifications.
Data will be loaded without transformation in one area of the AnalyticsPOC data store. The data will then be cleansed, merged, and transformed into a dimensional model The data load process must ensure that the raw and cleansed data is updated completely before populating the dimensional model.
The dimensional model must contain a date dimension. There is no existing data source for the date dimension. The Litware fiscal year matches the calendar year. The date dimension must always contain dates from 2010 through the end of the current year.
The product pricing group logic must be maintained by the analytics engineers in a single location. The pricing group data must be made available in the data store for T-SOL. queries and in the default semantic model. The following logic must be used:
- List prices that are less than or equal to 50 are in the low pricing group.
- List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
- List prices that are greater than 1,000 are in the high pricing group.
Security Requirements
Only Fabric administrators and the analytics team must be able to see the Fabric items created as part of the PoC.
Litware identifies the following security requirements for the Fabric items in the AnalyticsPOC workspace:
- Fabric administrators will be the workspace administrators.
- The data engineers must be able to read from and write to the data store. No access must be granted to datasets or reports.
- The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
- The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook
- The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.
- The date dimension must be available to all users of the data store.
- The principle of least privilege must be followed.
Both the default and custom semantic models must include only tables or views from the dimensional model in the data store. Litware already has the following Microsoft Entra security groups:
- FabricAdmins: Fabric administrators
- AnalyticsTeam: All the members of the analytics team
- DataAnalysts: The data analysts on the analytics team
- DataScientists: The data scientists on the analytics team
- DataEngineers: The data engineers on the analytics team
- AnalyticsEngineers: The analytics engineers on the analytics team
Report Requirements
The data analysts must create a customer satisfaction report that meets the following requirements:
- Enables a user to select a product to filter customer survey responses to only those who have purchased that product.
- Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected dat.
- Shows data as soon as the data is updated in the data store.
- Ensures that the report and the semantic model only contain data from the current and previous year.
- Ensures that the report respects any table-level security specified in the source data store.
- Minimizes the execution time of report queries.
Which type of data store should you recommend in the AnalyticsPOC workspace?
a data lake
a warehouse
a lakehouse
an external Hive metastore
Answer is a lakehouse
As a concept it exists in Fabric and is the most logical response for the given requirements,
Technical Requirements
Semi-structured and unstructured data
Files loaded by the data engineers to OneLake will be stored in the Parquet format and will meet Delta Lake specifications.
A. a data lake
As an answer it could be valid, but it is not really a concept that we have in Fabric.
B. a warehouse
It is not valid, because there is unstructured data.
D. an external Hive metastore
Has no sense.
The Hive Metastore is a centralized repository that stores metadata related to the tables, partitions, columns, schemas, and other data structures used by Hive and Spark. Contains information about the PHYSICAL location of the data, the schemas of the tables, and the relationships between them.
Overview
Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.
Existing Environment
Fabric Environment
Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.
Available Data
Litware has data that must be analyzed as shown in the following table.
The Product data contains a single table and the following columns.
Response
For each survey submitted, the following occurs:
- One row is added to the Survey table.
- One row is added to the Response table for each question in the survey.
- The Question table contains the text of each survey question. The third question in each survey response is an overall satisfaction score. Customers can submit a survey after each purchase.
User Problems
The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.
Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.
Requirements
Planned Changes
Litware plans to enable Fabric features in the existing tenant. The analytics team will create a new data store as a proof of concept (PoC). The remaining Liware users will only get access to the Fabric features once the PoC is complete. The PoC will be completed by using a Fabric trial capacity
The following three workspaces will be created:
- AnalyticsPOC: Will contain the data store, semantic models, reports pipelines, dataflow, and notebooks used to populate the data store
- DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake
- DataSciPOC: Will contain all the notebooks and reports created by the data scientists
The following will be created in the AnalyticsPOC workspace:
- A data store (type to be decided)
A custom semantic model
A default semantic model
Interactive reports
The data engineers will create data pipelines to load data to OneLake either hourly or daily depending on the data source. The analytics engineers will create processes to ingest, transform, and load the data to the data store in the AnalyticsPOC workspace daily. Whenever possible, the data engineers will use low-code tools for data ingestion. The choice of which data cleansing and transformation tools to use will be at the data engineers’ discretion.
All the semantic models and reports in the Analytics POC workspace will use the data store as the sole data source.
Technical Requirements
The data store must support the following:
- Read access by using T-SQL or Python
- Semi-structured and unstructured data
- Row-level security (RLS) for users executing T-SQL queries
Files loaded by the data engineers to OneLake will be stored in the Parquet format and will meet Delta Lake specifications.
Data will be loaded without transformation in one area of the AnalyticsPOC data store. The data will then be cleansed, merged, and transformed into a dimensional model The data load process must ensure that the raw and cleansed data is updated completely before populating the dimensional model.
The dimensional model must contain a date dimension. There is no existing data source for the date dimension. The Litware fiscal year matches the calendar year. The date dimension must always contain dates from 2010 through the end of the current year.
The product pricing group logic must be maintained by the analytics engineers in a single location. The pricing group data must be made available in the data store for T-SOL. queries and in the default semantic model. The following logic must be used:
- List prices that are less than or equal to 50 are in the low pricing group.
- List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
- List prices that are greater than 1,000 are in the high pricing group.
Security Requirements
Only Fabric administrators and the analytics team must be able to see the Fabric items created as part of the PoC.
Litware identifies the following security requirements for the Fabric items in the AnalyticsPOC workspace:
- Fabric administrators will be the workspace administrators.
- The data engineers must be able to read from and write to the data store. No access must be granted to datasets or reports.
- The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
- The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook
- The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.
- The date dimension must be available to all users of the data store.
- The principle of least privilege must be followed.
Both the default and custom semantic models must include only tables or views from the dimensional model in the data store. Litware already has the following Microsoft Entra security groups:
- FabricAdmins: Fabric administrators
- AnalyticsTeam: All the members of the analytics team
- DataAnalysts: The data analysts on the analytics team
- DataScientists: The data scientists on the analytics team
- DataEngineers: The data engineers on the analytics team
- AnalyticsEngineers: The analytics engineers on the analytics team
Report Requirements
The data analysts must create a customer satisfaction report that meets the following requirements:
- Enables a user to select a product to filter customer survey responses to only those who have purchased that product.
- Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected dat.
- Shows data as soon as the data is updated in the data store.
- Ensures that the report and the semantic model only contain data from the current and previous year.
- Ensures that the report respects any table-level security specified in the source data store.
- Minimizes the execution time of report queries.
Which two actions should you recommend performing from the Fabric Admin portal?
Enable the Users can try Microsoft Fabric paid features option for the entire organization.
Enable the Users can try Microsoft Fabric paid features option for specific security groups.
Enable the Allow Azure Active Directory guest users to access Microsoft Fabric option for specific security groups.
Enable the Users can create Fabric items option and exclude specific security groups.
Enable the Users can create Fabric items option for specific security groups.
Answers are;
B. Enable the Users can try Microsoft Fabric paid features option for specific security groups.
E. Enable the Users can create Fabric items option for specific security groups.
The POC is only for a certain group of users.
Overview
Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.
Existing Environment
Fabric Environment
Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.
Available Data
Litware has data that must be analyzed as shown in the following table.
The Product data contains a single table and the following columns.
Response
For each survey submitted, the following occurs:
- One row is added to the Survey table.
- One row is added to the Response table for each question in the survey.
- The Question table contains the text of each survey question. The third question in each survey response is an overall satisfaction score. Customers can submit a survey after each purchase.
User Problems
The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.
Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.
Requirements
Planned Changes
Litware plans to enable Fabric features in the existing tenant. The analytics team will create a new data store as a proof of concept (PoC). The remaining Liware users will only get access to the Fabric features once the PoC is complete. The PoC will be completed by using a Fabric trial capacity
The following three workspaces will be created:
- AnalyticsPOC: Will contain the data store, semantic models, reports pipelines, dataflow, and notebooks used to populate the data store
- DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake
- DataSciPOC: Will contain all the notebooks and reports created by the data scientists
The following will be created in the AnalyticsPOC workspace:
- A data store (type to be decided)
A custom semantic model
A default semantic model
Interactive reports
The data engineers will create data pipelines to load data to OneLake either hourly or daily depending on the data source. The analytics engineers will create processes to ingest, transform, and load the data to the data store in the AnalyticsPOC workspace daily. Whenever possible, the data engineers will use low-code tools for data ingestion. The choice of which data cleansing and transformation tools to use will be at the data engineers’ discretion.
All the semantic models and reports in the Analytics POC workspace will use the data store as the sole data source.
Technical Requirements
The data store must support the following:
- Read access by using T-SQL or Python
- Semi-structured and unstructured data
- Row-level security (RLS) for users executing T-SQL queries
Files loaded by the data engineers to OneLake will be stored in the Parquet format and will meet Delta Lake specifications.
Data will be loaded without transformation in one area of the AnalyticsPOC data store. The data will then be cleansed, merged, and transformed into a dimensional model The data load process must ensure that the raw and cleansed data is updated completely before populating the dimensional model.
The dimensional model must contain a date dimension. There is no existing data source for the date dimension. The Litware fiscal year matches the calendar year. The date dimension must always contain dates from 2010 through the end of the current year.
The product pricing group logic must be maintained by the analytics engineers in a single location. The pricing group data must be made available in the data store for T-SOL. queries and in the default semantic model. The following logic must be used:
- List prices that are less than or equal to 50 are in the low pricing group.
- List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
- List prices that are greater than 1,000 are in the high pricing group.
Security Requirements
Only Fabric administrators and the analytics team must be able to see the Fabric items created as part of the PoC.
Litware identifies the following security requirements for the Fabric items in the AnalyticsPOC workspace:
- Fabric administrators will be the workspace administrators.
- The data engineers must be able to read from and write to the data store. No access must be granted to datasets or reports.
- The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
- The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook
- The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.
- The date dimension must be available to all users of the data store.
- The principle of least privilege must be followed.
Both the default and custom semantic models must include only tables or views from the dimensional model in the data store. Litware already has the following Microsoft Entra security groups:
- FabricAdmins: Fabric administrators
- AnalyticsTeam: All the members of the analytics team
- DataAnalysts: The data analysts on the analytics team
- DataScientists: The data scientists on the analytics team
- DataEngineers: The data engineers on the analytics team
- AnalyticsEngineers: The analytics engineers on the analytics team
Report Requirements
The data analysts must create a customer satisfaction report that meets the following requirements:
- Enables a user to select a product to filter customer survey responses to only those who have purchased that product.
- Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected dat.
- Shows data as soon as the data is updated in the data store.
- Ensures that the report and the semantic model only contain data from the current and previous year.
- Ensures that the report respects any table-level security specified in the source data store.
- Minimizes the execution time of report queries.
You need to implement the date dimension in the data store. The solution must meet the technical requirements.
What are two ways to achieve the goal?
Populate the date dimension table by using a dataflow.
Populate the date dimension table by using a Copy activity in a pipeline.
Populate the date dimension view by using T-SQL.
Populate the date dimension table by using a Stored procedure activity in a pipeline.
Answers are;
A. Populate the date dimension table by using a dataflow.
D. Populate the date dimension table by using a Stored procedure activity in a pipeline.
As per the technical requirements;
"The dimensional model must contain a date dimension. There is no existing data source for the date dimension. The Litware fiscal year matches the calendar year. The date dimension must always contain dates from 2010 through the end of the current year."
No existing data source & The date dimension must always contain dates from 2010 through the end of the current year is the Key Point here using the elimination method View wont be appropriate and COPY activity cant be used since there is no data source for DATE table.
Overview
Litware, Inc. is a manufacturing company that has offices throughout North America. The analytics team at Litware contains data engineers, analytics engineers, data analysts, and data scientists.
Existing Environment
Fabric Environment
Litware has been using a Microsoft Power BI tenant for three years. Litware has NOT enabled any Fabric capacities and features.
Available Data
Litware has data that must be analyzed as shown in the following table.
The Product data contains a single table and the following columns.
Response
For each survey submitted, the following occurs:
- One row is added to the Survey table.
- One row is added to the Response table for each question in the survey.
- The Question table contains the text of each survey question. The third question in each survey response is an overall satisfaction score. Customers can submit a survey after each purchase.
User Problems
The analytics team has large volumes of data, some of which is semi-structured. The team wants to use Fabric to create a new data store.
Product data is often classified into three pricing groups: high, medium, and low. This logic is implemented in several databases and semantic models, but the logic does NOT always match across implementations.
Requirements
Planned Changes
Litware plans to enable Fabric features in the existing tenant. The analytics team will create a new data store as a proof of concept (PoC). The remaining Liware users will only get access to the Fabric features once the PoC is complete. The PoC will be completed by using a Fabric trial capacity
The following three workspaces will be created:
- AnalyticsPOC: Will contain the data store, semantic models, reports pipelines, dataflow, and notebooks used to populate the data store
- DataEngPOC: Will contain all the pipelines, dataflows, and notebooks used to populate OneLake
- DataSciPOC: Will contain all the notebooks and reports created by the data scientists
The following will be created in the AnalyticsPOC workspace:
- A data store (type to be decided)
A custom semantic model
A default semantic model
Interactive reports
The data engineers will create data pipelines to load data to OneLake either hourly or daily depending on the data source. The analytics engineers will create processes to ingest, transform, and load the data to the data store in the AnalyticsPOC workspace daily. Whenever possible, the data engineers will use low-code tools for data ingestion. The choice of which data cleansing and transformation tools to use will be at the data engineers’ discretion.
All the semantic models and reports in the Analytics POC workspace will use the data store as the sole data source.
Technical Requirements
The data store must support the following:
- Read access by using T-SQL or Python
- Semi-structured and unstructured data
- Row-level security (RLS) for users executing T-SQL queries
Files loaded by the data engineers to OneLake will be stored in the Parquet format and will meet Delta Lake specifications.
Data will be loaded without transformation in one area of the AnalyticsPOC data store. The data will then be cleansed, merged, and transformed into a dimensional model The data load process must ensure that the raw and cleansed data is updated completely before populating the dimensional model.
The dimensional model must contain a date dimension. There is no existing data source for the date dimension. The Litware fiscal year matches the calendar year. The date dimension must always contain dates from 2010 through the end of the current year.
The product pricing group logic must be maintained by the analytics engineers in a single location. The pricing group data must be made available in the data store for T-SOL. queries and in the default semantic model. The following logic must be used:
- List prices that are less than or equal to 50 are in the low pricing group.
- List prices that are greater than 50 and less than or equal to 1,000 are in the medium pricing group.
- List prices that are greater than 1,000 are in the high pricing group.
Security Requirements
Only Fabric administrators and the analytics team must be able to see the Fabric items created as part of the PoC.
Litware identifies the following security requirements for the Fabric items in the AnalyticsPOC workspace:
- Fabric administrators will be the workspace administrators.
- The data engineers must be able to read from and write to the data store. No access must be granted to datasets or reports.
- The analytics engineers must be able to read from, write to, and create schemas in the data store. They also must be able to create and share semantic models with the data analysts and view and modify all reports in the workspace.
- The data scientists must be able to read from the data store, but not write to it. They will access the data by using a Spark notebook
- The data analysts must have read access to only the dimensional model objects in the data store. They also must have access to create Power BI reports by using the semantic models created by the analytics engineers.
- The date dimension must be available to all users of the data store.
- The principle of least privilege must be followed.
Both the default and custom semantic models must include only tables or views from the dimensional model in the data store. Litware already has the following Microsoft Entra security groups:
- FabricAdmins: Fabric administrators
- AnalyticsTeam: All the members of the analytics team
- DataAnalysts: The data analysts on the analytics team
- DataScientists: The data scientists on the analytics team
- DataEngineers: The data engineers on the analytics team
- AnalyticsEngineers: The analytics engineers on the analytics team
Report Requirements
The data analysts must create a customer satisfaction report that meets the following requirements:
- Enables a user to select a product to filter customer survey responses to only those who have purchased that product.
- Displays the average overall satisfaction score of all the surveys submitted during the last 12 months up to a selected dat.
- Shows data as soon as the data is updated in the data store.
- Ensures that the report and the semantic model only contain data from the current and previous year.
- Ensures that the report respects any table-level security specified in the source data store.
- Minimizes the execution time of report queries.
You need to ensure the data loading activities in the AnalyticsPOC workspace are executed in the appropriate sequence. The solution must meet the technical requirements.
What should you do?
Create a dataflow that has multiple steps and schedule the dataflow.
Create and schedule a Spark notebook.
Create and schedule a Spark job definition.
Create a pipeline that has dependencies between activities and schedule the pipeline.
Answer is Create a pipeline that has dependencies between activities and schedule the pipeline.
Pipeline can ensure the activities follow the required sequence.
Reference:
https://learn.microsoft.com/en-us/fabric/data-factory/activity-overview#data-transformation-activities
Overview
Contoso, Ltd. is a US-based health supplements company. Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sales and Retail Sales. The Research division assigns internally developed product lines to individual teams of researchers and analysts.
Existing Environment
Identity Environment
Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroup1 and ResearchReviewersGroup2.
Data Environment
Contoso has the following data environment:
- The Sales division uses a Microsoft Power BI Premium capacity.
- The semantic model of the Online Sales department includes a fact table named Orders that uses Import made. In the system of origin, the OrderID value represents the sequence in which orders are created.
- The Research department uses an on-premises, third-party data warehousing product.
- Fabric is enabled for contoso.com.
- An Azure Data Lake Storage Gen2 storage account named storage1 contains Research division data for a product line named Productline1. The data is in the delta format.
- A Data Lake Storage Gen2 storage account named storage2 contains Research division data for a product line named Productline2. The data is in the CSV format.
Requirements
Planned Changes
Contoso plans to make the following changes:
- Enable support for Fabric in the Power BI Premium capacity used by the Sales division.
- Make all the data for the Sales division and the Research division available in Fabric.
- For the Research division, create two Fabric workspaces named Productline1ws and Productine2ws.
- In Productline1ws, create a lakehouse named Lakehouse1.
- In Lakehouse1, create a shortcut to storage1 named ResearchProduct.
Data Analytics Requirements
Contoso identifies the following data analytics requirements:
- All the workspaces for the Sales division and the Research division must support all Fabric experiences.
- The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing.
- The Research division workspaces must be grouped together logically to support OneLake data hub filtering based on the department name.
- For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints.
- For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer.
- All the semantic models and reports for the Research division must use version control that supports branching.
Data Preparation Requirements
Contoso identifies the following data preparation requirements:
- The Research division data for Productline1 must be retrieved from Lakehouse1 by using Fabric notebooks.
- All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.
Semantic Model Requirements
Contoso identifies the following requirements for implementing and managing semantic models:
- The number of rows added to the Orders table during refreshes must be minimized.
- The semantic models in the Research division workspaces must use Direct Lake mode.
General Requirements
Contoso identifies the following high-level requirements that must be considered for all solutions:
- Follow the principle of least privilege when applicable.
- Minimize implementation and maintenance effort when possible.
You need to recommend which type of Fabric capacity SKU meets the data analytics requirements for the Research division.
What should you recommend?
A
EM
P
F
Answer is F
Requirement: The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing.
Why: Microsoft Fabric Capacity (Pay-as-you-go or Reservation)
Get a shared pool of capacity that powers all capabilities in Microsoft Fabric, from data modeling and data warehousing to business intelligence and AI experiences (one minute minimum).
It is F license. Fabric capacity SKUs all start with F.
Reference:
https://azure.microsoft.com/en-us/pricing/details/microsoft-fabric/
Overview
Contoso, Ltd. is a US-based health supplements company. Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sales and Retail Sales. The Research division assigns internally developed product lines to individual teams of researchers and analysts.
Existing Environment
Identity Environment
Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroup1 and ResearchReviewersGroup2.
Data Environment
Contoso has the following data environment:
- The Sales division uses a Microsoft Power BI Premium capacity.
- The semantic model of the Online Sales department includes a fact table named Orders that uses Import made. In the system of origin, the OrderID value represents the sequence in which orders are created.
- The Research department uses an on-premises, third-party data warehousing product.
- Fabric is enabled for contoso.com.
- An Azure Data Lake Storage Gen2 storage account named storage1 contains Research division data for a product line named Productline1. The data is in the delta format.
- A Data Lake Storage Gen2 storage account named storage2 contains Research division data for a product line named Productline2. The data is in the CSV format.
Requirements
Planned Changes
Contoso plans to make the following changes:
- Enable support for Fabric in the Power BI Premium capacity used by the Sales division.
- Make all the data for the Sales division and the Research division available in Fabric.
- For the Research division, create two Fabric workspaces named Productline1ws and Productine2ws.
- In Productline1ws, create a lakehouse named Lakehouse1.
- In Lakehouse1, create a shortcut to storage1 named ResearchProduct.
Data Analytics Requirements
Contoso identifies the following data analytics requirements:
- All the workspaces for the Sales division and the Research division must support all Fabric experiences.
- The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing.
- The Research division workspaces must be grouped together logically to support OneLake data hub filtering based on the department name.
- For the Research division workspaces, the members of ResearchReviewersGroup1 must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints.
- For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer.
- All the semantic models and reports for the Research division must use version control that supports branching.
Data Preparation Requirements
Contoso identifies the following data preparation requirements:
- The Research division data for Productline1 must be retrieved from Lakehouse1 by using Fabric notebooks.
- All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.
Semantic Model Requirements
Contoso identifies the following requirements for implementing and managing semantic models:
- The number of rows added to the Orders table during refreshes must be minimized.
- The semantic models in the Research division workspaces must use Direct Lake mode.
General Requirements
Contoso identifies the following high-level requirements that must be considered for all solutions:
- Follow the principle of least privilege when applicable.
- Minimize implementation and maintenance effort when possible.
You need to migrate the Research division data for Productline1. The solution must meet the data preparation requirements.
How should you complete the code?
Check the answer section
Answer is delta & Tables/productline1
Requirements: Use managed tables.
If you use saveAsTable() you don't need to specify the path "Table/"
If you you save() you specify the full path