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| Section | Weight | Objectives |
|---|---|---|
| Maintain a data analytics solution | 25โ30 | - Implement security and governance
|
| Prepare data for analytics | 45โ50 | - Clean and transform data
|
| Implement and manage semantic models | 25โ30 | - Design and build semantic models
|
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NEW QUESTION # 196
Case Study 2 - Litware, Inc
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.
The customer satisfaction data contains the following tables:
- Survey
- Question
- 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?
Answer: B
Explanation:
The data store must handle semi-structured and unstructured data, therefore a Lakehouse should be the optimal solution supporting read access with T-SQL and Python.
NEW QUESTION # 197
You have a semantic model named Model1 that contains data that relates to customers and their bank account balances.
Model1 has the following tables and columns.
A customer can have one or more accounts. Each account can be associated to multiple customers.
You need to ensure that users can query Model1 to identify the total transaction amounts by customer.
What should you add to Model1?
Answer: B
Explanation:
Scenario:
Tables:
DimAccount (accounts)
FactTransaction (transactions)
DimCustomer (customers)
Business rule:
A customer can have multiple accounts.
An account can be linked to multiple customers.
Many-to-many relationship.
Requirement: Calculate total transaction amounts by customer.
Analysis:
Direct many-to-many between FactTransaction and DimCustomer would not properly resolve aggregation.
Adding CustomerKey directly in FactTransaction is not possible because transactions link to accounts, not customers directly.
Correct approach: Create a bridge table (CustomerAccount) that maps Customer # Account.
Then link FactTransaction # DimAccount # Bridge # DimCustomer.
NEW QUESTION # 198
Case Study 1 - Contoso
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?
Answer: D
Explanation:
A is loading the data from the ResearchProduct table stored in Delta format within the Tables directory of your lakehouse.
NEW QUESTION # 199
You have a Fabric tenant that contains a warehouse.
A user discovers that a report that usually takes two minutes to render has been running for 45 minutes and has still not rendered.
You need to identify what is preventing the report query from completing.
Which dynamic management view (DMV) should you use?
Answer: A
Explanation:
The correct DMV to identify what is preventing the report query from completing is sys.
dm_pdw_exec_requests (D). This DMV is specific to Microsoft Analytics Platform System (previously known as SQL Data Warehouse), which is the environment assumed to be used here. It provides information about all queries and load commands currently running or that have recently run. References = You can find more about DMVs in the Microsoft documentation for Analytics Platform System.
NEW QUESTION # 200
You have a Fabric tenant that contains a lakehouse named Lakehouse1. You plan to use Dataflow Gen2 to ingest and transform data from an Azure SQL Database into Lakehouse1.
Which language should you use to transform the data in the dataflow?
Answer: C
NEW QUESTION # 201
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