D.   

Share each report to the Azure Active Directory group of the sales department

Explanation:
This question tests the correct method for granting report access to a group of users (the sales department) while adhering to Power BI's security and governance best practices. The core principle is to manage access centrally and efficiently.

Correct Option:

D. Share each report to the Azure Active Directory group of the sales department.
This is the most secure, manageable, and scalable approach. Sharing reports directly with an Azure AD group ensures access is controlled through centralized group membership. It avoids permission sprawl, simplifies user management (users inherit access via the group), and aligns with the principle of least privilege by granting access only to the specific reports needed, not the entire workspace.

Incorrect Options:

A. Add the sales department as a member of the reports workspace.
Adding users as Members grants them excessive permissions within the workspace (they can modify reports, datasets, and publish content). For consumers who only need to view reports, this violates the principle of least privilege and creates a significant security and governance risk.

B. Add the Azure Active Directory group as an Admin of the reports workspace.
This grants the highest level of permission (Admin), including the ability to delete all content, manage all user access, and modify everything. This is grossly over-permissive for a department that simply needs to view reports.

C. Distribute an app to the users in the Azure Active Directory group.
While distributing an App is an excellent method for broad, read-only distribution, the question states "share each report." Sharing individual reports is more precise when the sales department does not need the entire curated content of a published app, or when different reports might need to be shared with different groups.

Reference:
Microsoft Learn, "Distribute a report or dashboard" and "Roles in workspaces." Best practice dictates using Azure AD groups for sharing to manage permissions efficiently and using the Share function for direct report access, reserving workspace roles for content creators and managers.

D.   

From Power Query, add a date table. Create an active relationship to the OrderDate
column in the Orders table and an inactive relationship to the ShippedDate column in the
Orders table.

Explanation:
The question is about designing a data model to support reporting on dates from the Orders table (like OrderDate and ShippedDate). The requirement implies needing separate analysis by calendar attributes (quarter, month) for at least the order date. The best practice is to create a proper date dimension table.

Correct Option:

D. From Power Query, add a date table. Create an active relationship to the OrderDate column in the Orders table and an inactive relationship to the ShippedDate column in the Orders table.
This is the correct dimensional modeling approach. A dedicated date table provides consistent calendar attributes (year, quarter, month, week) for filtering and grouping. An active relationship to OrderDate allows standard reporting by order date. An inactive relationship to ShippedDate enables time-based analysis on shipping date using the USERELATIONSHIP() function in DAX measures when needed, offering maximum flexibility and performance.

Incorrect Options:

A. From Power Query, use a DAX expression to add columns...
This is incorrect. While possible, adding calculated columns for multiple date attributes directly to the fact table is poor practice. It bloats the fact table, lacks reusability, and doesn't create a proper star schema. Using Power Query (M), not DAX, is the preferred method for such transformations in the data preparation layer.

B. From Power Query, add columns to the Orders table...
This is a partial and suboptimal solution. While using Power Query is correct for transformation, adding columns only for OrderDate ignores the potential need to analyze by ShippedDate. More importantly, it violates star schema design by embedding dimensional attributes in the fact table, which hurts performance and manageability compared to using a separate date dimension.

C. From Power BI Desktop, use the Auto date/time option.
The Auto date/time feature creates hidden date tables, which are not recommended for enterprise models. They increase model size, offer limited customization, and can lead to confusion. Explicitly creating a date table is a core exam objective and a documented best practice.

Reference:
Microsoft Learn, "Model data in Power BI" and "Create date tables in Power BI." Guidance strongly recommends creating a dedicated date table as a best practice for time intelligence. Using active and inactive relationships from a single date table to multiple date columns in a fact table is a standard modeling pattern.

B.   Filter out discontinued products while importing the Product table.

Explanation:
The goal is to reduce the size of the dataset while preserving all reporting requirements. Dataset size is impacted by the number of rows (cardinality) and columns loaded into memory. The optimal strategy removes unnecessary data at import.

Correct Option:

B. Filter out discontinued products while importing the Product table.
This directly reduces the number of rows stored in the model by removing products not needed for active reporting. If reports are only required for active products, filtering out discontinued items during the data import in Power Query is the most effective way to shrink the dataset without losing required information. This decreases memory usage and improves refresh and query performance.

Incorrect Options:

A. Change the OrderID column to text data type.
This would increase the dataset size. Text data types consume significantly more memory than numeric data types (like Integer). Changing a high-cardinality ID column from integer to text would inflate the model size, which is the opposite of the goal.

C. Remove the QuantityPerUnit column from the Products table.
While removing an unused column can reduce size, its impact is minor compared to filtering rows. The question emphasizes meeting "report requirements." If any report needs this column, removing it would break that requirement. Option B (filtering rows) typically offers greater size reduction.

D. Group the Categories table by the CategoryID column.
This is a nonsensical action in the context of minimizing a dataset in Power BI. A GROUP BY operation in Power Query is used for aggregation, not compression. It would likely destroy the dimensional structure of the Categories table, making it unusable for reporting, and is not a valid method for reducing model size.

Reference:
Microsoft Learn guidance on "Performance optimization" and "Data reduction techniques" for Power BI emphasizes filtering rows early in the process (in Power Query) as a primary method for reducing data volume and improving model performance.

Correct Sequence and Actions:
Select the line chart.

You must first select the visual (the line chart) you want to modify. This activates the Visualizations pane, which gives you access to the Analytics tab needed for the next steps.

From the Analytics pane, add an Average line.

The requirement is to "display the mean monthly order count." In Power BI's Analytics pane, the Average line adds a constant line showing the arithmetic mean of the values in the chart. This visually establishes the threshold.

Turn on data labels for the new line.

After adding the Average line, you should enable its Data labels. This ensures the exact numeric value of the mean is displayed on the chart, making it clear and meeting the "display" requirement explicitly.

Explanation of the Full Task:
The goal is to enhance the line chart to visually highlight months where performance exceeds the average. The "mean monthly order count" is a single, constant value calculated across all data points in the visual. Power BI's built-in Analytics feature for "Average line" is designed precisely for this purpose, creating a static horizontal reference line at the mean value. Enabling data labels shows the number.

Why the Other Actions are Incorrect:

Create a 12-month rolling average quick measure...: A rolling average calculates a moving benchmark, not a single, constant mean for all months. It would create a second, moving line, not a static reference line for comparison.

From the Analytics pane, add a Median line: The requirement specifies the mean (average), not the median. While similar, the median is a different statistical measure and would not satisfy the stated requirement.

Reference:
Microsoft Learn: "Analytics pane in Power BI" details how to add constant lines like Average to visuals. This feature automatically calculates the mean of the chart's values and provides the option to display data labels.

C.   

From the properties of the dataset, modify the Q&A and Cortana settings.

Explanation:
The problem is that users need to find one of four specific, similarly named reports ("Sales Data1," etc.) using natural language queries in Q&A. Since the reports differ by time period and underlying business context, users must be able to ask questions like "show sales from 2013 to 2015" and get directed to the correct "Sales Data1" report. This requires enhancing the dataset's semantic model for Q&A.

Correct Option:

C. From the properties of the dataset, modify the Q&A and Cortana settings.
Within the dataset's Q&A and Cortana settings, you can add synonyms, descriptive phrases, and Q&A hints to the underlying tables and fields. You can tailor this so that terms like "Contoso ownership period" or "2013 to 2015 sales" are semantically linked to the "Sales Data1" report's data, guiding Q&A to surface the correct visual or report page when those natural terms are queried.

Incorrect Options:

A. From the properties of the dataset, create four Featured Q&A Questions.
Featured Q&A Questions are pre-written example questions that appear in the Q&A box to guide users. While helpful, they do not fundamentally teach Q&A that "sales from the Contoso era" refers to a specific subset of data. They provide shortcuts but do not enhance the semantic understanding of the data model itself.

B. From the Format settings of the reports, modify the Page Information.
Page Information (like display name and tooltip) helps users in reading a report, but it does not influence how the Q&A engine interprets natural language queries against the underlying dataset. Q&A queries the semantic model, not report page metadata.

D. From the properties of the workspace, modify the Language Settings.
This controls the display language (like English or Spanish) for the workspace interface, not the semantic relationships and synonyms used by the Q&A engine to interpret user queries about specific data contexts.

Reference:
Microsoft Learn: "Improve the quality of Q&A results in Power BI." This documentation explains using Q&A setup at the dataset (semantic model) level to add synonyms, terms, and phrasings, which is essential for mapping business-specific language (e.g., "the lawsuit period") to the correct underlying data.

C.   Field value

Explanation:
This question focuses on the different ways to apply conditional formatting in Power BI. When you need to change the appearance of a visual based on specific numerical thresholds, you typically use the "FX" button next to a property (like Callout Value color). Field Value formatting is unique because it allows you to delegate the logic to a DAX measure. This approach is highly reusable across multiple visuals and reports, ensuring consistent branding and logic application without manually configuring rules in every instance.

Correct Option:

Option C (Field Value):
This is the correct choice when you want to use a DAX measure to define colors (e.g., using Hex codes or color names). By creating a measure like Color = IF([Value] >= 100, "DarkRed", "DarkGray"), you can apply this logic to the card visual. It minimizes effort by centralizing the logic; if the threshold or colors need to change later, you update the measure once rather than editing every visual’s internal rules.

Incorrect Option:

Option A (Color Scale):
This is incorrect because Color Scales create a gradient or "heatmap" effect. It transitions smoothly between a minimum and maximum value. It is not designed for "hard stops" or binary logic like "above/below 100," making it unsuitable for specific threshold-based color changes.

Option B (Rules):
While Rules are often used for this, Field Value is often considered more scalable for enterprise reports. Rules require manual entry of "Greater than" and "Less than" values within the UI for every visual. If the requirement is to minimize design effort across a complex report, DAX-based Field Values are more efficient.

Reference:
Microsoft Learn: "Conditional formatting in tables, matrices, and cards" describes these options. Rules are the primary method for applying formatting based on thresholds or ranges for the visual's field value itself, which matches this scenario perfectly.

D.   

Too many records were sent to the visual.

Explanation:
R visuals in the Power BI service have a few limitations including:
Data size limitations – data used by the R visual for plotting is limited to 150,000
rows. If more than 150,000 rows are selected, only the top 150,000 rows are used
and a message is displayed on the image. Additionally, the input data has a limit of
250 MB.
Reference:
https://docs.microsoft.com/en-us/power-bi/visuals/service-r-visuals

D.   

Create an active relationship between Sales and Date for the purchase date and an
inactive relationship for the ship date

Explanation:
Only one relationship can be active.
Note: If you query two or more tables at the same time, when the data is loaded, Power BI
Desktop attempts to find and create relationships for you. The relationship options
Cardinality, Cross filter direction, and Make this relationship active are automatically set.
Reference:
https://docs.microsoft.com/en-us/power-bi/transform-model/desktop-create-and-managerelationships

C.   

Member

C.   

In the Axis field, add Country at the top, followed by ProvState, and then City.

Explanation: References:
https://docs.microsoft.com/en-us/power-bi/guided-learning/visualizations#step-18
https://docs.microsoft.com/en-us/power-bi/power-bi-visualization-drill-down

A.   

From the Microsoft Office 365 Admin center, and the Power BI (free) subscription, and then assign a license to each user.

Explanation:
References: http://www.nubo.eu/en/blog/2016/12/Enable-PowerBI-On-Office-365/

C.   

In the Store table query, use the Table.TrasformColumnTypes function.