Option b, while it may seem effective, requires manual intervention and does not leverage the automation capabilities of Power BI, which can lead to errors and inconsistencies if the data source is updated frequently. Option c, using DAX functions, is a reactive approach that addresses the issue after the data has been imported, which is less efficient than resolving the problem at the source. Lastly, option d, which suggests excluding entries with null values, would lead to a loss of potentially valuable data and could skew the analysis. By transforming the data in Power Query, the analyst not only resolves the immediate issue but also sets up a repeatable process for future data imports, enhancing the overall reliability and accuracy of the analytics solution. This approach aligns with best practices in data preparation and ensures that the visualizations created in Power BI reflect the true sales performance across all regions.

Furthermore, Azure Role-Based Access Control (RBAC) is essential for managing permissions and ensuring that only authorized personnel can access sensitive data. RBAC allows for the assignment of roles to users, groups, and applications, enabling fine-grained access control based on the principle of least privilege. This means that users are granted only the permissions necessary to perform their job functions, significantly reducing the risk of unauthorized access to sensitive information. In contrast, enabling public access to Azure Blob Storage (as suggested in option b) would expose sensitive data to anyone on the internet, which is contrary to GDPR requirements. Relying solely on Azure’s built-in encryption features without implementing additional access controls (as in option c) would leave the organization vulnerable to data breaches, as encryption alone does not prevent unauthorized access. Lastly, storing sensitive data in a non-compliant third-party service (as in option d) would not only violate GDPR but also undermine the organization’s commitment to data protection and compliance. Thus, the combination of Azure Key Vault for key management and Azure RBAC for access control represents a comprehensive approach to securing sensitive data in compliance with GDPR, making it the most suitable choice for the organization.

Simultaneously, the line graphs illustrate sales trends over time, allowing viewers to observe patterns, fluctuations, and overall growth or decline in sales. This combination of visual elements caters to different cognitive processing styles; some viewers may find it easier to interpret categorical data through bars, while others may prefer the continuous nature of line graphs for trend analysis. In contrast, the principle of data-ink ratio emphasizes minimizing non-essential ink used in a visualization, which is not directly related to the choice of visual elements in this scenario. The principle of chartjunk avoidance focuses on eliminating unnecessary visual elements that do not contribute to the data’s message, while color theory pertains to the effective use of color to convey information, which is also not the primary focus here. Thus, the analyst’s decision to use both bar charts and line graphs effectively leverages dual encoding to enhance the dashboard’s clarity and effectiveness, ensuring that the audience can quickly grasp both the current performance and historical trends in sales data. This approach aligns with best practices in data visualization, which advocate for clarity, efficiency, and the ability to convey complex information in an accessible manner.

To establish this relationship in a database or analytics tool like Power BI, you would link the `ProductID` in the `Products` table (the “one” side) to the `ProductID` in the `Sales` table (the “many” side). This allows for accurate aggregation of sales data by product category, enabling you to calculate total sales per category effectively. If a many-to-many relationship were established, it would complicate the analysis, as it would imply that a single sale could relate to multiple products, which is not the case in this context. A one-to-one relationship would also be incorrect, as it would suggest that each sale corresponds to a unique product, which is not true given that multiple sales can occur for the same product. Lastly, a self-referencing relationship is irrelevant here, as it pertains to relationships within the same table rather than between two distinct tables. Thus, understanding the nature of the data and the relationships between entities is crucial for effective data modeling and analysis in business intelligence scenarios. This foundational knowledge allows analysts to create accurate reports and insights that drive decision-making processes.

In contrast, using a standard SQL query to calculate totals each time the report is generated can lead to performance bottlenecks, especially with large datasets, as it requires scanning the entire sales transactions table repeatedly. Storing raw sales data in a non-indexed table and performing aggregations directly would also be inefficient, as it would require full table scans, leading to longer query execution times. Implementing a clustered index on the sales transactions table could improve performance for certain queries, but it does not inherently provide the same level of efficiency as a materialized view for aggregation purposes. While indexes can speed up data retrieval, they do not pre-aggregate data, which is essential for the scenario described. Thus, the most efficient approach for summarizing sales data by product and month in Azure Synapse Analytics is to utilize a materialized view, as it optimizes both performance and storage by pre-computing and storing the aggregated results.

Publishing the report to the Power BI service and setting it to “Public” would expose the report to anyone with the link, which is not suitable for sensitive financial data. Similarly, exporting the report as a PDF and emailing it does not provide a secure method of sharing, as the PDF can be forwarded or accessed by unintended recipients. Lastly, creating a Power BI app and distributing it to all users within the organization could lead to unauthorized access, as it does not restrict access based on user roles or needs. By leveraging the sharing capabilities of Power BI, the analyst can ensure that the report is only accessible to the intended audience, thus adhering to best practices for data security and compliance with regulations such as GDPR or HIPAA, which mandate strict controls over sensitive information. This approach not only protects the data but also facilitates collaboration among stakeholders who require access to the report for decision-making purposes.

The option of using a standard view that calculates total sales on-the-fly would not be efficient for performance, especially with millions of records, as it requires computation every time a query is run. This could lead to increased load times and strain on the database resources. Implementing a clustered index on the sales table without any aggregation would improve the performance of queries that filter or sort based on the indexed columns, but it would not address the need for pre-aggregated data, which is crucial for reporting. Creating a non-clustered index on the product category column and running a daily ETL process to aggregate sales data could improve performance, but it would not be as efficient as using a materialized view. The ETL process would also introduce complexity and potential delays in data availability. In summary, the best approach for optimizing query performance while ensuring that the aggregated data is updated regularly is to use a materialized view. This method allows for efficient querying of pre-aggregated data, reducing the computational overhead during reporting and ensuring timely access to relevant insights.

In contrast, performing a full backup and restore (option b) would require significant downtime, as the database would need to be taken offline during the backup process, and the restore could take a considerable amount of time, especially with 10 TB of data. Migrating during off-peak hours (option c) may help reduce the impact on users, but it does not address the core issue of downtime during the migration process. Lastly, using a third-party tool to export data to CSV files (option d) is not advisable for such a large database, as it can lead to data loss, integrity issues, and would also require extensive manual effort to import the data back into Azure SQL Database. Thus, the best strategy for this scenario is to leverage the online migration feature of Azure DMS, which is specifically designed to handle such migrations with minimal disruption and maximum data integrity.

Once the predictive model is developed and trained, it can be integrated with Power BI. This integration allows the company to visualize the forecasted sales data dynamically, enabling stakeholders to make informed decisions based on real-time insights. Power BI’s visualization capabilities enhance the interpretability of the model’s output, making it easier for users to understand trends and patterns. In contrast, the other options present less effective strategies. Importing raw data directly into Power BI without preprocessing or modeling limits the analysis to basic historical trends, which may not capture the underlying factors influencing sales. Relying solely on DAX functions for forecasting does not utilize the advanced analytics capabilities of Azure, which can significantly enhance the accuracy of predictions. Lastly, creating a static report without predictive modeling fails to provide actionable insights for future sales, rendering the analysis ineffective for strategic planning. Thus, the combination of Azure Machine Learning for model development and Power BI for visualization represents the most comprehensive and effective approach to predictive analytics in this scenario. This method not only enhances the accuracy of forecasts but also ensures that the insights derived from the data are actionable and relevant to the company’s strategic goals.

Moreover, the hierarchical namespace supports data lifecycle management by allowing policies to be applied at different levels of the directory structure. This means that data can be automatically moved to lower-cost storage tiers or deleted based on its age or relevance, optimizing storage costs and ensuring that only necessary data is retained. In contrast, the other options present misconceptions about the capabilities of ADLS. For instance, while ADLS does support efficient data ingestion, it does not automatically partition data into flat files; rather, it allows users to define their own partitioning strategies based on their specific use cases. Additionally, while data redundancy and high availability are important features of Azure services, they are not directly related to the hierarchical namespace. Finally, ADLS does not inherently provide data transformation capabilities; instead, it is often used in conjunction with services like Azure Data Factory or Azure Databricks for such tasks. Thus, understanding the hierarchical namespace’s role in data organization and management is critical for leveraging Azure Data Lake Storage effectively in analytics solutions.

Nested templates enable the organization of complex deployments into manageable components. Each nested template can represent a specific layer of the application, and dependencies can be explicitly defined using the `dependsOn` property. This ensures that resources are deployed in the correct order, which is crucial for multi-tier applications where the business logic layer may depend on the database layer being available before it can be deployed. In contrast, deploying all resources in a single ARM template without defining dependencies can lead to deployment failures if resources are not created in the required order. While Azure does attempt to manage the order of deployment, relying solely on this can introduce risks, especially in complex applications. Using Azure CLI commands to deploy resources individually is less efficient and does not leverage the full capabilities of ARM templates, such as version control and repeatability. Finally, creating separate resource groups for each layer and deploying them independently can complicate management and does not provide the necessary interdependencies that a multi-tier application requires. By using nested ARM templates, the company can ensure that their deployment is not only consistent across different environments but also scalable and maintainable, adhering to best practices in Azure resource management. This approach aligns with the principles of Infrastructure as Code (IaC), allowing for versioning and easier updates in the future.

When using DirectQuery, the data remains in the AAS model, and Power BI sends queries to AAS based on user interactions with the report. This means that users will only see data they are authorized to view, as the security roles applied in AAS will filter the data accordingly. This is particularly important in scenarios where sensitive financial data is involved, as it prevents unauthorized access. In contrast, importing data into Power BI (as suggested in option b) would require the analyst to manage row-level security within Power BI, which can lead to complexities and potential security oversights. Additionally, using a live connection without security roles (option c) compromises data security, while relying on scheduled refreshes (option d) can lead to stale data and does not leverage the real-time capabilities of AAS. Thus, the optimal solution is to connect Power BI to Azure Analysis Services using DirectQuery mode, ensuring that both performance and security requirements are met effectively. This approach not only enhances user experience through dynamic filtering but also aligns with best practices for data governance in enterprise environments.

Exporting the report to PDF format and emailing it lacks security measures, as it does not allow for dynamic data updates and can lead to unauthorized access if the email is forwarded. Sharing the report link publicly compromises data security, as it exposes sensitive information to anyone with the link, violating compliance regulations. Lastly, publishing the report to a shared drive and granting access to all employees does not provide the necessary granularity in data access control, potentially leading to unauthorized viewing of sensitive data. By utilizing Power BI’s built-in features like RLS, the analyst can ensure that the report is both secure and tailored to the needs of specific stakeholders, thus maintaining compliance with data governance standards while facilitating effective communication of the quarterly performance metrics. This method not only protects sensitive information but also enhances the overall user experience by providing relevant insights to each stakeholder based on their role within the organization.

On the other hand, batch processing involves collecting data over a period and processing it in groups, which introduces latency. This method is less suitable for real-time analytics, as it may lead to delays in reporting and decision-making. Data replication, while useful for maintaining copies of data across systems, does not inherently provide real-time ingestion capabilities. Similarly, data archiving focuses on storing historical data for long-term retention rather than facilitating immediate access and analysis. In this scenario, the retail company’s requirement for low latency and high throughput aligns perfectly with stream processing. This technique not only supports real-time data ingestion but also scales efficiently to handle varying data loads, ensuring that the company can respond quickly to changing market conditions and customer behaviors. By leveraging stream processing, the company can enhance its analytics capabilities, leading to more informed decision-making and improved customer experiences.

On the other hand, Azure Stream Analytics is designed specifically for real-time data ingestion and processing. It can ingest data from sources like social media feeds and IoT devices, enabling the company to analyze customer behavior as it happens. This dual capability allows the retail company to maintain a comprehensive view of customer interactions, combining both real-time insights and historical trends. The other options present limitations. Solely using Azure Blob Storage for batch data ingestion would not provide the necessary real-time analytics capabilities, as it is primarily a storage solution without built-in processing features. Implementing only Azure Event Hubs would focus exclusively on streaming data, neglecting the batch processing needed for historical analysis. Lastly, relying solely on SQL Server Integration Services (SSIS) would limit the company’s ability to scale and adapt to real-time data ingestion needs, as SSIS is primarily designed for ETL processes in traditional data warehousing scenarios. Thus, the hybrid approach not only meets the immediate needs for real-time analytics but also ensures that the company can leverage historical data for deeper insights, making it the most suitable choice for their data ingestion strategy.

In contrast, manually calculating average sales per month lacks the sophistication of statistical forecasting and does not account for seasonality or trends, making it less reliable. Creating a separate Excel model for forecasting introduces unnecessary complexity and potential data integrity issues, as it requires additional steps to ensure the data is accurately reflected in Power BI. Lastly, using a pie chart to represent sales distribution does not contribute to forecasting; it merely visualizes a snapshot of data without any predictive capabilities. Therefore, the most effective method for the company to achieve its goal of forecasting future sales is to utilize Power BI’s built-in forecasting feature, which enhances the analytical capabilities of the report while providing actionable insights based on historical data.

Increasing the number of data entry personnel may seem like a solution to handle high transaction volumes, but it does not address the root cause of data quality issues. More personnel could lead to more variability in data entry practices unless they are trained to follow the established protocols. Relying solely on automated data entry systems without human oversight can exacerbate data quality problems, as automated systems may not be able to handle exceptions or nuances in data that require human judgment. Lastly, creating separate databases for each product category could lead to fragmentation of data, making it difficult to analyze overall sales trends and potentially introducing further inconsistencies. In summary, the most effective strategy for the retail company is to establish a standardized data entry protocol combined with regular audits, as this approach directly addresses the issues of consistency and completeness in their sales records. This aligns with best practices in data quality management, which emphasize the importance of both preventive measures and ongoing monitoring to maintain high data quality standards.

Implementing measures for KPIs is crucial as it allows for dynamic calculations based on the filtered data. For instance, to calculate total sales, average order value, and sales growth percentage, the company can use DAX (Data Analysis Expressions) to create measures that respond to the slicer selections. This ensures that the KPIs displayed are relevant to the user’s current selections, providing real-time insights. Moreover, optimizing data models by using aggregations and establishing relationships between tables is essential for performance. A well-structured data model reduces the load time of the dashboard and improves the responsiveness of the visualizations. By creating relationships between fact and dimension tables, the dashboard can efficiently retrieve and display data without redundancy. In contrast, creating multiple static reports for each region and product category would lead to a fragmented user experience and increased maintenance overhead. Using a single table without relationships would hinder the ability to filter and analyze data effectively, while limiting the dashboard to only one KPI would not provide a comprehensive view of the sales performance. Therefore, the best approach combines interactive filtering, dynamic KPI measures, and an optimized data model to create a robust and user-friendly dashboard.

DAX queries are crucial for data manipulation and calculation in Power BI, and inefficient queries can lead to slow report performance. Community members often share their own optimizations, which can provide insights into more efficient coding practices or alternative approaches to data modeling. Furthermore, community forums often feature discussions on common pitfalls and performance bottlenecks, allowing analysts to learn from the mistakes of others. In contrast, posting a question without context (option b) is unlikely to yield useful responses, as community members need specific details to provide relevant advice. Relying solely on official documentation (option c) can limit the analyst’s understanding, as documentation may not cover all practical scenarios encountered by users. Lastly, ignoring community feedback (option d) disregards the collaborative nature of problem-solving in data analytics, which can stifle innovation and improvement. Thus, the most effective strategy involves leveraging community insights to optimize DAX queries and data models, ultimately leading to enhanced report performance. This approach not only fosters a collaborative learning environment but also empowers analysts to implement proven strategies that have been validated by their peers.

The bar chart displaying total sales by product category can be created using measures that aggregate sales data based on the selected year from the slicer. This ensures that the visualizations are responsive to user inputs, providing a seamless experience. In contrast, importing data into Power BI would require manual refreshes, which does not satisfy the requirement for real-time updates. Additionally, using a combination of DirectQuery and Import modes without real-time updates would not fully leverage the capabilities of Power BI for dynamic data visualization. Lastly, creating a dashboard in Excel and linking it to Power BI would not provide the same level of interactivity and real-time data capabilities that Power BI offers natively. Thus, the approach that combines DirectQuery, a year slicer, and a dynamically updating bar chart is the most effective way to achieve the desired functionality in the dashboard. This method aligns with best practices for building interactive dashboards in Power BI, ensuring that users have access to the most current data while maintaining an engaging user experience.

This collaborative approach allows for a comprehensive understanding of the data lifecycle, from collection to analysis, and ensures that data quality, security, and accessibility are prioritized. Each department brings unique insights into how data is used and what challenges they face, which is essential for creating effective stewardship practices. On the other hand, assigning data ownership solely to the IT department limits the input from those who actually use the data for clinical and operational decisions. This can lead to a disconnect between data management practices and the actual needs of the organization. Similarly, implementing a centralized data management system without involving end-users can result in a lack of buy-in and may overlook critical usability issues. Lastly, focusing exclusively on compliance without considering the usability of data for analytics can hinder the organization’s ability to leverage data for informed decision-making, ultimately impacting patient care and operational efficiency. Thus, a holistic approach that integrates compliance with usability through a collaborative governance structure is essential for effective data stewardship and ownership in a healthcare setting.

Furthermore, the principle of purpose limitation requires that personal data collected for one purpose should not be used for another incompatible purpose without obtaining further consent from the data subjects. By establishing a data retention policy, the company can ensure that personal data is not kept longer than necessary and is only used for the purposes for which it was collected, thereby reducing the risk of non-compliance. In contrast, collecting excessive personal data (option b) contradicts the data minimization principle, as it involves gathering more information than is necessary. Using personal data for different purposes without consent (option c) violates the purpose limitation principle, and allowing unrestricted access to third-party vendors (option d) can lead to unauthorized processing and potential data breaches, which are also against GDPR regulations. Therefore, the correct approach is to implement a robust data retention policy that aligns with GDPR requirements, ensuring that personal data is handled responsibly and ethically.

In this scenario, the application is read-heavy with occasional writes. Strong consistency guarantees that reads always return the most recent committed write, which can lead to higher latency and reduced throughput, making it less suitable for a read-heavy workload. Eventual consistency, while providing the best performance and lowest latency, may not be appropriate for applications that require any level of data consistency, as it allows for temporary discrepancies between replicas. Bounded staleness offers a compromise by allowing reads to be slightly out-of-date while still providing a guarantee on the maximum staleness of the data. However, this model may introduce unnecessary complexity for the application if the staleness requirements are not well-defined. Session consistency, on the other hand, is particularly well-suited for scenarios where a single user interacts with the application. It ensures that within a session, a user will always read their own writes, providing a good balance between performance and consistency. This model allows for low-latency reads while ensuring that users have a coherent view of their data, which is essential for user profiles that may change frequently during a session. Thus, for a global application with a read-heavy workload and occasional writes, session consistency is the most appropriate choice, as it provides a strong user experience without sacrificing performance. This understanding of the nuances of consistency models in Azure Cosmos DB is critical for designing effective and efficient applications in a distributed environment.

In contrast, random sampling involves selecting a subset of data points from the original dataset, which may lead to loss of important information and does not inherently reduce the complexity of the dataset. Data aggregation, while useful for summarizing data, may not effectively reduce dimensionality as it often combines multiple data points into a single summary statistic, potentially losing granularity. Feature selection, on the other hand, involves identifying and retaining only the most relevant features from the dataset, which can also help reduce size but does not transform the data into a lower-dimensional space like PCA does. PCA is particularly advantageous in scenarios where the dataset has many correlated features, as it can effectively capture the underlying structure of the data in fewer dimensions. By applying PCA, the analyst can reduce the number of features while still preserving the essential information needed for subsequent analysis, thus improving the efficiency and performance of machine learning models. This method is widely used in various fields, including finance, healthcare, and marketing, where large datasets are common and dimensionality reduction is necessary for effective analysis.

When aggregations are used, the underlying dataset can be designed to pre-calculate and store summary values, such as totals or averages, which can be quickly retrieved when users interact with the report. This approach not only improves performance but also maintains data accuracy and integrity, as the aggregated data can still be refreshed regularly to reflect the latest information. In contrast, increasing the number of visuals on the report page may lead to further performance degradation, as each visual requires processing time and resources. Using direct query mode for all tables can provide real-time data access, but it often results in slower performance due to the need to query the database for every interaction. Lastly, adding more slicers may complicate the user experience and could lead to performance issues, as each slicer adds additional filtering logic that must be processed. Thus, the most effective approach to optimize the report’s performance while ensuring data accuracy is to implement aggregations in the dataset. This strategy strikes a balance between responsiveness and maintaining the integrity of the data presented in the report.

Batch data ingestion, while efficient for processing large volumes of data at once, introduces latency. For instance, if transaction data is ingested every hour, there is a window of time during which fraudulent activities could go unnoticed. This delay can be detrimental, especially in scenarios where immediate action is required to mitigate risks. A hybrid approach, which combines both batch and real-time processing, may seem appealing; however, it can complicate the architecture and introduce challenges in data consistency and latency management. Additionally, a manual data entry system is impractical in this context, as it would not only be inefficient but also prone to human error, further delaying the insights needed for effective decision-making. In summary, the choice of real-time data ingestion aligns with the need for immediate insights and responsiveness in the financial services industry, particularly for applications like fraud detection and customer behavior analysis. This approach leverages technologies such as Azure Stream Analytics or Apache Kafka, which are designed to handle high-velocity data streams, ensuring that the organization remains agile and informed in a rapidly changing environment.

Moreover, implementing data gateways is crucial for securely connecting on-premises data sources to the Power BI service. This ensures that data governance policies are adhered to, as sensitive data can remain within the organization’s network while still being accessible for reporting and analytics. The use of gateways also facilitates real-time data access, as they can be configured to refresh data at specified intervals or in real-time, thus minimizing latency. On the other hand, relying solely on Power BI Pro licenses and cloud-based data sources (option b) may not be feasible if the organization has critical data stored on-premises. This could lead to data silos and hinder comprehensive analytics. Deploying reports directly to the Power BI service without data gateways (option c) would limit the ability to access on-premises data, which is a significant drawback given the company’s diverse data landscape. Lastly, creating a single data model and publishing it without considering refresh strategies (option d) could lead to outdated information being presented to users, undermining the goal of providing real-time access. In summary, the hybrid deployment model with Power BI Premium and data gateways is the most effective strategy for ensuring scalability, security, and real-time data access while maintaining data governance across the organization.

The formula for calculating the average sales per customer in each region can be expressed as: $$ \text{Average Sales per Customer} = \frac{\text{SUM(TotalSales)}}{\text{COUNT(DISTINCT CustomerID)}} $$ This method ensures that the average is not skewed by the number of transactions, which could misrepresent the purchasing behavior of customers. In contrast, option b) fails to consider the number of customers, leading to an inaccurate average that does not reflect individual customer behavior. Option c) only uses transaction counts, which does not provide insight into customer-specific sales. Lastly, option d) focuses on product categories rather than customer regions, which is not aligned with the analysis goal. Therefore, the most effective approach is to join the `Sales` and `Customers` tables, group by `Region`, and calculate the average sales per customer accurately. This method provides a nuanced understanding of customer purchasing behavior across different regions, which is critical for strategic decision-making in retail analytics.

However, RBAC alone may not provide the granularity needed for specific datasets within the data lake. This is where ACLs come into play. ACLs allow for fine-grained access control at the file and folder level within ADLS. By setting ACLs, the company can specify which users or groups have read, write, or execute permissions on individual files or directories. This dual approach ensures that different teams can access only the datasets they need, thereby adhering to the principle of least privilege, which is a key aspect of data governance. In contrast, relying solely on Azure RBAC would not provide the necessary granularity for dataset-level access, while using only network security groups would not address the need for user-specific permissions. Additionally, managing access through a single shared access signature (SAS) for all data in a single container would pose significant security risks, as it could lead to unauthorized access to sensitive information. Therefore, the combination of Azure RBAC and ACLs is the most effective strategy for managing access control in Azure Data Lake Storage, ensuring both security and compliance.

The correct approach involves using the `SUMX` function, which iterates over each row in the `Sales` table. For each row, it calculates the discount amount by multiplying the `SalesAmount` by the `Discount` percentage. This is expressed as `Sales[SalesAmount] * Sales[Discount]`. The `SUMX` function then sums these individual discount amounts across all rows, providing the total discount applied. The total revenue can then be calculated by taking the total sales amount, which is obtained using `SUM(Sales[SalesAmount])`, and subtracting the total discounts calculated by `SUMX`. Therefore, the measure can be defined as: $$ \text{Total Revenue} = \text{SUM(Sales[SalesAmount])} – \text{SUMX(Sales, Sales[SalesAmount] \times Sales[Discount])} $$ This formula ensures that the total revenue reflects the actual sales after accounting for the discounts applied to each sale. In contrast, the other options present flawed logic. Option b) incorrectly subtracts the total discount directly from the total sales amount without considering the relationship between the discount and the sales amount. Option c) attempts to apply the discount directly to the sales amount but does not account for the row-wise calculation needed for accurate results. Option d) incorrectly applies an average discount to the total sales, which does not reflect the actual discounts applied on a per-transaction basis. Thus, understanding the nuances of DAX functions and their application is crucial for creating accurate measures in Power BI.