DP-600 Practice Exam Questions with Answers Implementing Analytics Solutions Using Microsoft Fabric Certification

The df.show() method also does not meet the goal. It is used to show the contents of the DataFrame, not to compute statistical functions. References = The usage of the show() function is documented in the PySpark API documentation.

For a Type 2 slowly changing dimension (SCD), you typically need to add the following types of columns that do not exist in the source system:

• An effective start date and time (E): This column records the date and time from which the data in the row is effective.
• An effective end date and time (A): This column indicates until when the data in the row was effective. It allows you to keep historical records for changes over time.
• A surrogate key (C): A surrogate key is a unique identifier for each row in a table, which is necessary for Type 2 SCDs to differentiate between historical and current records.

References: Best practices for designing slowly changing dimensions in data warehousing solutions, which include Type 2 SCDs, are commonly discussed in data warehousing and business intelligence literature and would be part of the modeling guidance in a Fabric tenant's documentation.

To reduce memory usage and refresh time, splitting the OrderDateTime into separate date and time columns (A) can help optimize the model because date/time data types can be more memory-intensive than separate date and time columns. Moreover, replacing TotalSalesAmount with a measure (D) instead of a calculated column ensures that the calculation is performed at query time, which can reduce the size of the model as the value is not stored but calculated on the fly. References = The best practices for optimizing Power BI models are detailed in the Power BI documentation, which recommends using measures for calculations that don't need to be stored and adjusting data types to improve performance.

To identify whether throttling is occurring, you should use the Monitoring hub (B). This provides a centralized place where you can monitor and manage the health, performance, and reliability of your data estate, and see if the compute resources are being throttled. References = The use of the Monitoring hub for performance management and troubleshooting is detailed in the Azure Synapse Analytics documentation.

When you need to return all rows that are present in both tables, you use a full outer join. This type of join combines the results of both left and right outer joins and returns all rows from both tables, with matching rows from both sides where available. If there is no match, the result is NULL on the side of the join where there is no match.

References: Information about joins and their use in querying data in a lakehouse can be typically found in the SQL and data processing documentation of the Fabric tenant or lakehouse solutions.

• Remove the files: Run the VACUUM command on a schedule.
• Combine the files: Set the optimizeWrite table setting. or Run the OPTIMIZE command on a schedule.

To remove files that are no longer used, the VACUUM command is used in Delta Lake to clean up invalid files from a table. To combine smaller files into larger ones, you can either set the optimizeWrite setting to combine files during write operations or use the OPTIMIZE command, which is a Delta Lake operation used to compact small files into larger ones.

To view column information like the one shown in the exhibit in Power Query, you need to select the options that enable profiling and display quality and distribution details. These are: A. Enable column profile - This option turns on profiling for each column, showing statistics such as distinct and unique values. B. Show column quality details - It displays the column quality bar on top of each column showing the percentage of valid, error, and empty values. E. Show column value distribution - It enables the histogram display of value distribution for each column, which visualizes how often each value occurs.

References: These features and their descriptions are typically found in the Power Query documentation, under the section for data profiling and quality features.

To meet the technical requirement that data loading activities must ensure the raw and cleansed data is updated completely before populating the dimensional model, you would need a mechanism that allows for ordered execution. A pipeline in Microsoft Fabric with dependencies set between activities can ensure that activities are executed in a specific sequence. Once set up, the pipeline can be scheduled to run at the required intervals (hourly or daily depending on the data source).

For the semantic model design required for the customer satisfaction report, the choices for data source authentication method and mode should be made based on security and performance considerations as per the case study provided.

Authentication method: The data should be accessed securely, and given that row-level security (RLS) is required for users executing T-SQL queries, you should use an authentication method that supports RLS. Service principal authentication is suitable for automated and secure access to the data, especially when the access needs to be controlled programmatically and is not tied to a specific user's credentials.

Mode: The report needs to show data as soon as it is updated in the data store, and it should only contain data from the current and previous year. DirectQuery mode allows for real-time reporting without importing data into the model, thus meeting the need for up-to-date data. It also allows for RLS to be implemented and enforced at the data source level, providing the necessary security measures.

Based on these considerations, the selections should be:

• Authentication method: Service principal authentication
• Mode: DirectQuery

The PoC is planned to be completed using a Fabric trial capacity, which implies that users involved in the PoC should be able to try paid features. However, this should be limited to specific security groups involved in the PoC to prevent the entire organization from accessing these features before the trial is proven successful (A). The ability for users to create Fabric items should also be enabled for specific security groups to ensure that only the relevant team members participating in the PoC can create items in the Fabric environment (E).

Both a dataflow (A) and a Stored procedure activity in a pipeline (B) are capable of creating and populating a date dimension table. A dataflow can perform the transformation needed to create the date dimension, and it aligns with the preference for using low-code tools for data ingestion when possible. A Stored procedure could be written to generate the necessary date dimension data and executed within a pipeline, which also adheres to the technical requirements for the PoC.

• The measure should use the AVERAGE function to calculate the average value.
• It should reference the Response Value column from the 'Survey' table.
• The 'Number of months' should be used to define the period for the average calculation.

To calculate the average overall satisfaction score using DAX, you would need to use the AVERAGE function on the response values related to satisfaction questions. The DATESINPERIOD function will help in calculating the rolling average over the last 12 months.

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A lakehouse (C) should be recommended for the AnalyticsPOC workspace. It combines the capabilities of a data warehouse with the flexibility of a data lake. A lakehouse supports semi-structured and unstructured data and allows for T-SQL and Python read access, fulfilling the technical requirements outlined for Litware. References = For further understanding, Microsoft's documentation on the lakehouse architecture provides insights into how it supports various data types and analytical operations.

• Data Engineers: Read All SQL analytics endpoint data
• Data Analysts: Read All Apache Spark
• Data Scientists: Read All SQL analytics endpoint data

The permissions for the data store in the AnalyticsPOC workspace should align with the principle of least privilege:

• Data Engineers need read and write access but not to datasets or reports.
• Data Analysts require read access specifically to the dimensional model objects and the ability to create Power BI reports.
• Data Scientists need read access via Spark notebooks. These settings ensure each role has the necessary permissions to fulfill their responsibilities without exceeding their required access level.

For ingesting customer data into the data store in the AnalyticsPOC workspace, a dataflow (D) should be recommended. Dataflows are designed within the Power BI service to ingest, cleanse, transform, and load data into the Power BI environment. They allow for the low-code ingestion and transformation of data as needed by Litware's technical requirements. References = You can learn more about dataflows and their use in Power BI environments in Microsoft's Power BI documentation.

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• You should use CREATE VIEW to make the pricing group logic available for T-SQL queries.
• The CASE statement should be used to determine the pricing group based on the list price.

The T-SQL statement should create a view that classifies products into pricing groups based on the list price. The CASE statement is the correct conditional logic to assign each product to the appropriate pricing group. This view will standardize the pricing group logic across different databases and semantic models.