The formula `AVERAGEX(FILTER(Sales, Sales[SalesDate] >= EDATE(TODAY(), -12)), Sales[SalesAmount])` effectively filters the `Sales` table to include only sales from the last year and then computes the average sales amount from this filtered dataset. In contrast, the second option, `AVERAGE(Sales[SalesAmount])`, does not apply any date filtering and would return the average sales amount for all records in the `Sales` table, which is not the desired outcome. The third option, while it uses `CALCULATE` to modify the context, incorrectly sets the date filter to one year ago from today without considering the full 12-month range. Lastly, the fourth option calculates a simple average by dividing the total sales amount by the count of sales, which again does not consider the time frame and would yield an incorrect average for the specified period. Thus, the correct DAX formula must incorporate both the filtering of the date range and the calculation of the average sales amount, making the first option the most suitable choice for this scenario.

The correct expression uses the `CALCULATE` function, which modifies the filter context of a calculation. In this case, `CALCULATE(SUM(Sales[Amount]), Sales[Date] >= DATE(YEAR(TODAY()), MONTH(TODAY())-3, 1))` effectively sums the `Sales[Amount]` while applying a filter that restricts the data to only include sales from the last quarter. The `DATE` function constructs a date that represents the first day of the month three months prior to the current month, ensuring that only relevant sales are included in the calculation. Option b, while it uses `SUMX` and `FILTER`, does not directly apply the filter context in the same way as `CALCULATE`, which is more efficient for this scenario. Option c is incorrect because DAX does not support the `WHERE` clause in this context; it must use functions like `CALCULATE` or `FILTER`. Lastly, option d, while it uses `CALCULATE`, incorrectly applies the filter using `FILTER` without specifying the correct context for the calculation, which could lead to unintended results. Thus, the correct expression effectively combines the necessary functions to achieve the desired outcome, demonstrating a nuanced understanding of how to manipulate both row and filter contexts in DAX.

Drill-through, on the other hand, is used to navigate to a detailed report page that focuses on a specific data point, which is not the primary need in this scenario. Bookmarks allow users to save specific views of reports but do not provide the dynamic filtering required for real-time analysis. Tooltips enhance the information displayed when hovering over data points but do not facilitate the filtering of data across visuals. Understanding the nuances of these features is essential for effective data analysis in Power BI. Cross-filtering not only improves the interactivity of reports but also empowers users to derive insights quickly by allowing them to focus on specific segments of data without needing to create multiple reports or visuals. This capability is particularly valuable in a business context where timely decision-making is critical. Thus, leveraging cross-filtering in this scenario aligns perfectly with the marketing team’s goal of analyzing sales trends based on their selected product category.

On the other hand, Direct Query provides real-time access to the data, which is beneficial for scenarios where up-to-the-minute data is critical. However, it comes with limitations, such as restrictions on the types of DAX functions that can be used and potential performance issues due to the need to query the data source for each interaction. This can lead to slower report performance, especially when complex aggregations are required. In this case, since the company aims to generate reports that include complex aggregations and historical trends, Data Import is the more suitable choice. It allows for the full utilization of Power BI’s analytical capabilities without the performance drawbacks associated with Direct Query. While a hybrid approach could be considered in some scenarios, it may not provide the optimal performance and flexibility needed for the specific requirements of this analysis. Therefore, the best approach for the company is to utilize Data Import to ensure efficient and effective data analysis.

By having separate gateways, the company can also optimize performance. Each gateway can be configured based on the specific needs and usage patterns of the department it serves. For instance, if one department has a higher volume of data queries, that gateway can be allocated more resources to handle the load effectively without impacting the performance of other departments. In contrast, installing a single gateway for all departments can lead to performance bottlenecks, especially if multiple departments are accessing large datasets simultaneously. Additionally, using a shared user account for all departments compromises security, as it becomes challenging to track who accessed what data and when. This lack of accountability can lead to compliance issues, especially in industries that are heavily regulated. Relying solely on a cloud-based data gateway may not be feasible for all organizations, particularly those with strict data governance policies that require on-premises solutions. Therefore, the most effective strategy is to implement multiple gateways, ensuring that each department’s unique requirements are met while maintaining high standards of security and performance. This configuration aligns with best practices for data gateway management, promoting both efficiency and data protection.

In contrast, increasing the number of visuals on the report page can lead to further performance degradation, as each visual may require its own query to the dataset. This can compound the load times rather than alleviate them. Similarly, using direct query mode for all tables can be detrimental because it forces Power BI to query the underlying data source in real-time for every interaction, which can be slow and inefficient, especially with large datasets. Lastly, while adding more slicers may seem beneficial for user interactivity, it can also lead to performance issues as each slicer adds complexity to the queries being executed. Thus, the most effective strategy for performance optimization in this scenario is to implement aggregations in the data model, as it directly addresses the issue of excessive data processing and enhances the report’s responsiveness. This technique aligns with best practices in Power BI performance optimization, ensuring that the report remains efficient and user-friendly.

The correct expression begins with `CALCULATE(SUM(Sales[SalesAmount])`, which sums the `SalesAmount` for the current year. The condition `YEAR(Sales[SalesDate]) = YEAR(TODAY())` ensures that we are only considering sales from the current year. Next, we need to filter the products based on their sales in the previous year. This is done using the `FILTER` function, which evaluates each row in the `Sales` table. Inside the `FILTER`, we use another `CALCULATE` function to count the number of times each product was sold in the previous year, specified by `YEAR(Sales[SalesDate]) = YEAR(TODAY()) – 1`. The condition `> 100` ensures that we only include products that were sold more than 100 times in the previous year. The other options present variations that either incorrectly reference the current year instead of the previous year, or they do not properly filter the products based on their sales count. For instance, option b incorrectly counts the sales for the current year instead of the previous year, while option c fails to correctly apply the filter context for the product sales count. Option d uses `SUMX` without the necessary filtering logic to ensure that only products meeting the sales threshold are included. Thus, the correct DAX expression effectively combines these elements to achieve the desired analysis, demonstrating a nuanced understanding of DAX functions and their application in a real-world scenario.

In contrast, using a calculated column in the Customers table to sum sales directly would not account for the dynamic nature of the data model and could lead to incorrect totals, as it would not filter the Orders table based on the customer context. Creating a relationship between the Products and Customers tables is unnecessary and does not directly contribute to the goal of summing sales by customer. Lastly, using a simple aggregation function without considering the relationships would yield a total sales figure that does not accurately reflect the sales per customer, as it would aggregate all sales indiscriminately. Thus, the most effective approach is to create a measure using SUMX, which respects the relationships in the data model and provides an accurate total sales amount for each customer. This method not only ensures accuracy but also enhances the report’s interactivity and responsiveness to filters applied in Power BI, allowing for a more insightful analysis of customer purchasing behavior.

$$ \text{Growth Rate} = \frac{\text{Current Month Sales} – \text{Previous Month Sales}}{\text{Previous Month Sales}} \times 100 $$ This formula effectively measures the change in sales from one month to the next, providing a percentage that indicates how much sales have increased or decreased relative to the previous month. In this scenario, with current month sales of $120,000 and previous month sales of $100,000, the calculation would be: $$ \text{Growth Rate} = \frac{120,000 – 100,000}{100,000} \times 100 = \frac{20,000}{100,000} \times 100 = 20\% $$ This indicates a 20% growth in sales compared to the previous month. For optimal clarity on the dashboard, representing this growth rate as a line chart is advisable. Line charts are particularly effective for visualizing trends over time, allowing stakeholders to easily observe patterns in sales growth across multiple months. This visualization can help the sales manager and other decision-makers quickly assess performance and make informed decisions based on historical data. In contrast, the other options present incorrect formulas or inappropriate visualization methods. For instance, using a pie chart to represent growth rates is misleading, as pie charts are best suited for showing parts of a whole rather than changes over time. Similarly, the incorrect formulas would yield inaccurate growth rates, leading to potential misinterpretations of the sales performance data. Thus, understanding the correct calculation and visualization methods is crucial for effective data analysis and decision-making in Power BI dashboards.

In the correct expression, `CALCULATE(SUM(Sales[SalesAmount]), YEAR(Sales[TransactionDate]) = YEAR(TODAY()), QUARTER(Sales[TransactionDate]) = 4)`, the `SUM` function aggregates the `SalesAmount` column. The `YEAR(Sales[TransactionDate]) = YEAR(TODAY())` condition ensures that only transactions from the current year are considered. The `QUARTER(Sales[TransactionDate]) = 4` condition filters the transactions to include only those that occurred in the fourth quarter (October, November, December). Option b is incorrect because it uses the `MONTH` function to filter for the last quarter, which is not accurate since it includes only the last three months of the year rather than the entire quarter. Option c incorrectly uses the `MONTH` function to filter for April, which is not part of the last quarter. Option d fails to consider the year and only filters by the month of April, which does not correspond to the last quarter. This question tests the understanding of DAX functions, the importance of context in calculations, and the ability to apply logical conditions to filter data accurately. Understanding how to manipulate date functions and aggregate data correctly is crucial for effective data analysis in Power BI.

On the other hand, range partitioning is useful when the data has a natural order, such as dates or numerical ranges. This allows for efficient querying of specific ranges of data, which can reduce the amount of data scanned during queries and thus lower costs. By combining both strategies, the analyst can tailor the partitioning scheme to the specific access patterns and distribution of the data, ensuring that queries are executed efficiently while also optimizing resource usage. Implementing only hash partitioning (option b) may lead to an even distribution of data but could result in inefficient querying if the data has a natural order that is not leveraged. Relying solely on range partitioning (option c) simplifies the loading process but may lead to uneven data distribution and performance issues during concurrent queries. Finally, avoiding partitioning altogether (option d) can lead to significant performance degradation as the dataset grows, making it difficult to manage and query effectively. Therefore, the combination of hash and range partitioning is the most effective strategy for optimizing both performance and cost in Azure Synapse Analytics.

By implementing a unified framework, the organization can ensure that all employees are trained on the same policies, reducing the likelihood of non-compliance due to misunderstandings or lack of knowledge. Additionally, a centralized approach facilitates better monitoring and auditing of data practices, which is vital for identifying potential vulnerabilities and addressing them proactively. In contrast, allowing regional offices to develop their own policies (option b) could lead to significant inconsistencies and gaps in compliance, as different regions may interpret regulations differently. A decentralized approach (option c) would further exacerbate this issue, as individual departments may prioritize their operational needs over compliance, increasing the risk of data breaches. Lastly, focusing solely on GDPR (option d) neglects the importance of other applicable regulations like HIPAA, which could result in severe penalties and damage to the organization’s reputation if not properly addressed. Thus, the most effective strategy is to implement a centralized data governance framework that balances compliance with operational efficiency, ensuring that all regulatory requirements are met while minimizing risks associated with data management.

When a report is created in Power BI Desktop, it can connect to various data sources, including SQL databases and Excel files. However, for the report to be refreshed daily, especially when it involves on-premises data sources, the Power BI Gateway is necessary. It enables scheduled refreshes, which can be configured in the Power BI Service. This ensures that the data in the reports is current and reflects any changes made in the underlying data sources. The Power BI Desktop is primarily used for report creation and does not handle data refreshes directly. The Power BI Service is where reports are published and shared, but without the gateway, it cannot access on-premises data sources for refreshes. The Power BI Report Server is an alternative for hosting reports on-premises but is not specifically designed for handling data refreshes from multiple sources in the same way as the Power BI Gateway. In summary, the Power BI Gateway is essential for enabling efficient data refreshes from on-premises sources, ensuring that the reports maintain performance and data integrity while reflecting the most current information. Understanding the roles of these components within the Power BI architecture is critical for any data analyst working with Power BI, particularly in environments with diverse data sources and refresh requirements.

\[ \text{Percentage Contribution} = \left( \frac{\text{Sales of Category}}{\text{Total Sales}} \right) \times 100 \] In this scenario, the sales of the Electronics category are $150,000, and the total sales for the quarter are $500,000. Plugging these values into the formula, we have: \[ \text{Percentage Contribution} = \left( \frac{150,000}{500,000} \right) \times 100 \] Calculating this gives: \[ \text{Percentage Contribution} = \left( 0.3 \right) \times 100 = 30\% \] This calculation shows that the Electronics category contributes 30% to the total sales. Additionally, understanding the average sales per transaction is crucial for interpreting the dashboard effectively. The average transaction value of $250 indicates that the company had 2,000 transactions during the quarter, calculated as follows: \[ \text{Total Transactions} = \frac{\text{Total Sales}}{\text{Average Transaction Value}} = \frac{500,000}{250} = 2,000 \] This information can help the company assess customer behavior and sales performance across different categories. The dashboard should effectively visualize these KPIs, allowing stakeholders to make informed decisions based on the data presented. In summary, the correct calculation of the percentage contribution of the Electronics category is essential for accurately reflecting its performance relative to other categories, and it highlights the importance of data visualization in business intelligence tools like Power BI.

The left axis can represent the sales figures, while the right axis can represent the number of promotional campaigns. This approach provides a clear visual comparison, allowing stakeholders to observe how changes in promotional activity correlate with sales trends over time. Creating two separate line charts (option b) could lead to a disjointed analysis, making it difficult to draw direct correlations between the two datasets. Overlaying promotional data as a shaded area (option c) could obscure the sales data, leading to misinterpretation. Lastly, using a stacked area chart (option d) would not effectively convey the relationship between the two datasets, as it would imply a cumulative total rather than a comparative analysis. In summary, a dual-axis line chart not only enhances clarity but also facilitates a nuanced understanding of how promotional campaigns impact sales, enabling better strategic decisions based on the visualized data. This method aligns with best practices in data visualization, ensuring that the audience can easily interpret the relationships between the datasets without confusion.

By setting an appropriate similarity threshold (for example, 80% similarity), the analyst can effectively consolidate these entries into a single representation, thereby maintaining the integrity of the dataset while ensuring that all relevant transactions are accounted for. This approach is more efficient than manual review, which can be time-consuming and prone to human error. On the other hand, simply removing all entries with any variation or relying solely on exact matches would lead to the loss of valuable customer data and potentially misrepresent the customer base. Therefore, employing fuzzy matching not only enhances the accuracy of the data cleaning process but also ensures that the final report reflects a true and comprehensive view of the customer information. This method aligns with best practices in data management, emphasizing the importance of data quality and accuracy in analytical reporting.

In Power BI, measures are dynamic calculations that respond to the context of the data being analyzed. By using a measure that sums the sales amount, the company can ensure that the total revenue reflects only the sales associated with each customer. This is crucial for understanding customer behavior and making informed business decisions. On the other hand, the other options present various shortcomings. For instance, a calculated column that multiplies the quantity of products ordered by their unit price in the Orders table does not provide a direct measure of total sales revenue per customer; instead, it calculates a value at the row level, which is less flexible for analysis. Counting the number of orders placed by each customer without considering the sales amount fails to provide insight into the actual revenue generated, which is the primary goal. Lastly, averaging the sales amount across all customers disregards individual purchasing behavior and can lead to misleading conclusions about customer value. Thus, the correct approach involves creating a measure that accurately reflects total sales revenue by summing the relevant amounts while filtering by customer, ensuring that the analysis is both precise and contextually relevant. This understanding of data relationships and measure creation is essential for effective data modeling in Power BI.

Using DAX (Data Analysis Expressions), the analyst can create a calculated column that utilizes the `IF` function to check for the column’s existence. For example, the formula could look like this: $$ NewColumn = IF(ISBLANK([MissingColumn]), “Default Value”, [MissingColumn]) $$ This formula ensures that if the column is missing, the report will not break; instead, it will display a default value, allowing the report to continue functioning. On the other hand, ignoring the error or manually editing the Excel file may lead to future complications, especially if the report is scheduled to refresh automatically. Creating a new report that avoids the Excel file altogether is also not a practical solution, as it does not address the underlying issue and may lead to unnecessary duplication of effort. By proactively implementing error handling strategies, such as conditional columns, analysts can create more robust reports that are resilient to data source changes and errors, ultimately enhancing the reliability of their data analysis efforts. This approach aligns with best practices in data management and ensures that stakeholders can still derive insights from the report, even in the face of data inconsistencies.

In contrast, exporting the visual as an image (option b) would result in a static representation that does not update with data changes, thus failing to meet the requirement for dynamic data visualization. Creating a static report page (option c) would also not allow for real-time updates, as it would not refresh with new data. Lastly, using the “Publish to web” feature (option d) does not involve pinning and would not provide the same level of interactivity or data refresh capabilities as pinning a visual directly from the report. This understanding of the pinning process and the importance of maintaining data connectivity is crucial for analysts who rely on Power BI for real-time insights and decision-making. By ensuring that visuals are pinned correctly with the appropriate settings, analysts can enhance their dashboards’ effectiveness and usability.

Scheduled refreshes are essential for datasets that do not use DirectQuery, as they allow Power BI to pull the latest data from the source at specified intervals. However, if the data is frequently changing, relying solely on daily refreshes may lead to outdated reports. By setting the refresh to occur every hour, the company can significantly reduce the window of time during which reports may display stale data. On the other hand, a manual refresh process (option b) places the burden on users, which can lead to inconsistencies and errors, as not all users may remember to refresh the dataset before viewing reports. Utilizing a dataflow that refreshes only once a week (option c) would not be sufficient for a retail environment where data changes frequently, and relying on default settings (option d) does not take into account the specific needs of the business, potentially leading to missed updates. In summary, the combination of hourly scheduled refreshes and DirectQuery mode provides a robust solution for maintaining up-to-date reports, thereby minimizing discrepancies and enhancing the reliability of data-driven decisions.

Power Query is essential for data extraction and transformation. It allows users to connect to different data sources, clean, and shape the data before loading it into the Power BI environment. This is particularly important when dealing with disparate data sources like SQL databases and Excel spreadsheets, as it ensures that the data is in a consistent format for analysis. Once the data is transformed, the Data Model component comes into play. This is where relationships between different data tables are established, enabling the analyst to create a coherent data structure that supports complex queries and calculations. The Data Model allows for the creation of measures and calculated columns using DAX (Data Analysis Expressions), which are crucial for deriving insights from the data, such as calculating total sales or average sales per category. Finally, Power BI Desktop is the primary tool for designing and visualizing reports. It provides a user-friendly interface for creating interactive dashboards and visualizations that can effectively communicate the sales performance segmented by product category and region. The combination of these components ensures that the data is not only accurately represented but also actionable for decision-making. In contrast, the other options include components that are either not directly related to the report creation process or serve different purposes. For instance, Power BI Service is primarily for sharing and collaborating on reports, while Power Automate and Power Apps focus on workflow automation and app development, respectively. Similarly, Power BI Report Server and Azure Analysis Services are more suited for enterprise-level reporting and data modeling rather than the initial report creation process. Therefore, understanding the specific roles of each component is crucial for effectively utilizing Power BI in data analysis tasks.

Using multiple contrasting colors for each visual element can lead to confusion and may overwhelm the viewer, detracting from the report’s overall effectiveness. Similarly, employing various font sizes and styles randomly can create a disjointed experience, making it difficult for users to follow the narrative of the report. Excessive animations and transitions, while they may seem engaging, can also distract from the content and lead to a frustrating user experience, especially if they hinder the viewer’s ability to focus on the data being presented. In summary, a consistent layout that employs a uniform color palette and font style is fundamental in creating a professional and user-friendly report. This practice not only enhances readability but also fosters a sense of familiarity and trust in the data being presented, ultimately leading to better decision-making based on the insights derived from the report.

Using separate visuals for each level, as suggested in option b, can lead to a fragmented user experience and may complicate the report’s layout. It can also increase the cognitive load on users, as they would need to interact with multiple visuals instead of a single cohesive hierarchy. Flattening the data structure, as mentioned in option c, would eliminate the benefits of hierarchical data navigation, making it difficult for users to analyze data at different levels of granularity. This approach would hinder the analytical capabilities of the report, as users would lose the context provided by the hierarchy. Implementing bookmarks to switch between different levels of detail, as proposed in option d, is not an efficient solution for this scenario. While bookmarks can be useful for navigating between different report views, they do not provide the dynamic interaction that a hierarchy offers. Users would have to manually select bookmarks, which is less intuitive than simply drilling down through a hierarchy. In summary, creating a hierarchy in the data model and utilizing it in visualizations is the most effective method for enabling drill-down functionality in Power BI. This approach not only optimizes performance but also enhances the overall user experience by allowing for intuitive navigation through the data.

The `FILTER` function is crucial here as it allows for the creation of a new table that only includes rows meeting specific criteria. In this case, the criteria are that the `Region` must be ‘North’ and the `Amount` must be greater than or equal to $500. The expression `FILTER(Sales, Sales[Region] = “North” && Sales[Amount] >= 500)` effectively filters the `Sales` table to include only those records that satisfy both conditions. The `CALCULATE` function then takes the sum of the `Sales[Amount]` column, but only for the rows that have passed through the filter created by the `FILTER` function. This results in a total that accurately reflects the sales in the ‘North’ region while adhering to the specified threshold. Option b, while it uses the `FILTER` function, does not apply the `CALCULATE` function, which is necessary for modifying the filter context in this scenario. Option c incorrectly uses the logical operator for the threshold, as it should be `>=` instead of `<`, and it also lacks the `FILTER` function. Option d simply sums all sales amounts without any filtering, which does not meet the requirements of the question. Thus, the correct approach is to use `CALCULATE` in conjunction with `FILTER` to ensure that both the region and the sales threshold are properly accounted for in the calculation. This demonstrates a nuanced understanding of how DAX functions interact and how to effectively manipulate filter contexts to achieve desired analytical outcomes.

In contrast, using a simple list format fails to provide the necessary context and depth, which can lead to confusion about how data is interconnected. Focusing solely on one data source neglects the importance of the other sources, which may contain critical information that impacts the overall analysis. Lastly, documenting data sources in a separate spreadsheet without linking them to the Power BI report creates a disconnect that can hinder users’ ability to trace data lineage and understand the report’s context. By adopting a structured approach through a data dictionary, the analyst ensures that all relevant information is captured, promoting transparency and enhancing the usability of the Power BI report for stakeholders. This practice aligns with best practices in data governance and documentation, which emphasize the importance of comprehensive and clear data lineage documentation in analytics.

The best approach involves utilizing a limited color palette that incorporates high-contrast colors alongside shades of the company’s primary colors. This strategy ensures that all visual elements are not only visually appealing but also easily distinguishable from one another. High contrast is essential for accessibility, as it allows users with visual impairments to differentiate between various data points and understand the information being presented without confusion. Moreover, adhering to a uniform style guide across all visual elements fosters a sense of familiarity and professionalism, which is vital in business reporting. Consistency in design helps users navigate the report more intuitively, as they can predict how different data will be represented based on their previous interactions with the report. In contrast, applying a variety of colors without considering contrast can lead to confusion and misinterpretation of data, particularly for users with color vision deficiencies. Using only grayscale tones, while accessible, would completely disregard the branding aspect, making the report less engaging and potentially diminishing the company’s identity. Lastly, implementing different color schemes for each visual type can create a disjointed experience, making it difficult for users to follow the narrative of the data presented. Thus, the most effective design strategy is one that harmonizes branding with accessibility, ensuring that the report is both visually consistent and user-friendly.

In contrast, increasing the frequency of full dataset refreshes (option b) would likely exacerbate the problem, as full refreshes require processing the entire dataset, which can be time-consuming and resource-intensive. This could lead to even longer wait times for report availability. Reducing the dataset size by removing historical data (option c) may seem like a viable option, but it can compromise the analytical capabilities of the reports. Historical data is often essential for trend analysis and forecasting, which are critical for inventory decisions. Scheduling refreshes during peak business hours (option d) is counterproductive, as it can lead to performance bottlenecks when user demand is high. Instead, refreshes should ideally be scheduled during off-peak hours to minimize the impact on system performance and ensure that reports are available promptly when needed. Overall, the incremental data refresh strategy not only optimizes the refresh process but also maintains the integrity and usefulness of the dataset for ongoing analysis and reporting. This approach aligns with best practices in data management and ensures that the data analyst can provide timely insights to support business decisions.

In this scenario, the total sales amount is $500,000, and the sales for the ‘Electronics’ category is $120,000. The calculation would be as follows: 1. Calculate the total sales for ‘Electronics’: $$ \text{Sales for Electronics} = 120,000 $$ 2. Calculate the total sales across all categories: $$ \text{Total Sales} = 500,000 $$ 3. Now, apply the DAX formula: $$ \text{Percentage of Total Sales for Electronics} = \frac{120,000}{500,000} = 0.24 $$ 4. To express this as a percentage, multiply by 100: $$ 0.24 \times 100 = 24\% $$ This measure allows the company to dynamically calculate the percentage of total sales for any product category, providing valuable insights into the performance of different segments of their product line. The other options either do not correctly calculate the percentage of total sales or do not utilize the appropriate DAX functions to ensure accurate results. For instance, option b) simply divides the sales amount by itself, which will always yield 1 (or 100% when multiplied by 100), regardless of the category. Option c) incorrectly multiplies the sales amount by 100 before dividing, leading to an inflated percentage. Option d) uses a filter that restricts the calculation to the ‘Electronics’ category only, which does not provide the context of total sales needed for a percentage calculation. Thus, the correct measure is essential for accurate data analysis in Power BI.

The `DATESINPERIOD` function is crucial here as it generates a table of dates that spans the last three months from the maximum date present in the `SalesDate` column. The parameters of `DATESINPERIOD` are as follows: the date column to evaluate, the end date (which is the maximum date in the `SalesDate` column), the number of intervals (in this case, -3 for the last three months), and the interval type (MONTH). This effectively filters the sales data to only include records from the last three months. Option b simply sums the `SalesAmount` without any date filtering, which would not yield the desired result of sales for the last quarter. Option c uses the `FILTER` function but incorrectly applies the `EDATE` function, which does not account for the maximum date context and could lead to inaccurate results. Option d uses `SUMX`, which iterates over the `Sales` table but does not apply any time filtering, thus failing to meet the requirement of calculating sales for the last three months. In summary, the correct DAX expression effectively combines the `CALCULATE`, `SUM`, and `DATESINPERIOD` functions to accurately compute the total sales for the last quarter, demonstrating a nuanced understanding of DAX time intelligence functions and their application in real-world scenarios.

The `DATESINPERIOD` function is crucial here as it generates a table of dates that spans the last three months from the maximum date present in the `SalesDate` column. The parameters of `DATESINPERIOD` are as follows: the date column to evaluate, the end date (which is the maximum date in the `SalesDate` column), the number of intervals (in this case, -3 for the last three months), and the interval type (MONTH). This effectively filters the sales data to only include records from the last three months. Option b simply sums the `SalesAmount` without any date filtering, which would not yield the desired result of sales for the last quarter. Option c uses the `FILTER` function but incorrectly applies the `EDATE` function, which does not account for the maximum date context and could lead to inaccurate results. Option d uses `SUMX`, which iterates over the `Sales` table but does not apply any time filtering, thus failing to meet the requirement of calculating sales for the last three months. In summary, the correct DAX expression effectively combines the `CALCULATE`, `SUM`, and `DATESINPERIOD` functions to accurately compute the total sales for the last quarter, demonstrating a nuanced understanding of DAX time intelligence functions and their application in real-world scenarios.