The process described is descriptive analytics. This type of analytics focuses on summarizing historical data to understand what has happened. By creating column charts to show the distribution of retained versus lost customers based on various attributes, you are summarizing and visualizing past events (customer churn) to gain insights into the characteristics of these customer groups. The goal is to describe the data, which is the fundamental purpose of descriptive analytics.

A. prescriptive: This is incorrect because prescriptive analytics recommends specific actions to take. The scenario only involves visualizing data, not suggesting what to do about the churn.

B. diagnostic: This is incorrect because diagnostic analytics aims to understand why something happened. While the charts might be a first step, the act of creating them is descriptive, not an investigation of root causes.

D. predictive: This is incorrect because predictive analytics uses statistical models to forecast future outcomes. The scenario does not involve building a model to predict which customers will churn in the future.

1. Microsoft Learn

"Introduction to data science with Microsoft Fabric": Under the "Types of analytics" section

it defines descriptive analytics as helping "answer questions about what has happened in the past." It explicitly states

"Most business reporting

such as sales reports

marketing metrics

and social media analytics

is descriptive." The activity in the question—creating charts from historical data—is a form of business reporting.

Source: Microsoft Learn. Introduction to data science with Microsoft Fabric. Section: "Types of analytics".

2. Microsoft Power BI Documentation

"What is business intelligence?": This document outlines the four types of analytics. It defines descriptive analytics as answering "What happened?" and tracking key performance indicators (KPIs) to understand the present state of a business

which aligns with visualizing customer churn distributions.

Source: Microsoft Power BI. What is business intelligence?. Section: "The four types of data analytics".

In Microsoft Fabric, the CREATE TABLE AS CLONE OF T-SQL statement creates a zero-copy clone of a warehouse table.

• Statement 1 (Yes): Cloning is a metadata-only operation. It creates a new table definition that points to the existing data files in OneLake without physically copying the data, making it near-instantaneous regardless of table size.
• Statement 2 (No): The cloned table is an independent entity. Schema changes (DDL) applied to the source table after the clone is created do not propagate to the clone.
• Statement 3 (No): Data changes (DML) are isolated. The clone represents the state of the data at the precise moment of creation (Time Travel). Subsequent inserts, updates, or deletes in the source table do not affect the clone, and vice versa.

Microsoft Learn. (2024). Clone table in Microsoft Fabric. "What is a table clone?" and "Table clone capabilities". Available at: https://learn.microsoft.com/en-us/fabric/data-warehouse/clone-table

Relevant Quote: "A zero-copy clone is a replica of the table... creates a copy of the metadata while referencing the same data files in OneLake."

Relevant Quote: "The clone is an independent table... Changes to the source table (data or schema) are not reflected in the clone."

The default Power BI semantic model for a Lakehouse is automatically synchronized with its corresponding SQL analytics endpoint. This endpoint is responsible for discovering tables in the Lakehouse and exposing them for querying and modeling. The setting to control whether new tables are automatically added to the default semantic model is located within the configuration of the SQL analytics endpoint. By navigating to the SQL analytics endpoint settings, you can disable the "Sync the default Power BI semantic model" option, which prevents any further automatic updates and stops new tables from appearing in the model.

A. the semantic model settings

These settings manage the model's internal structure (e.g., measures, relationships) but do not control the automatic discovery of new tables from its data source.

B. the Lakehouse1 settings

These settings pertain to the Lakehouse storage layer and its properties, not the synchronization behavior of the associated SQL endpoint or the default semantic model.

C. the workspace settings

Workspace settings apply to the entire workspace (e.g., permissions, Git integration) and do not offer granular control over individual artifacts like a specific default semantic model.

1. Microsoft Learn. (2024). Default Power BI semantic models in Microsoft Fabric. "The default semantic model stays in sync with the lakehouse by automatically initiating a sync whenever a new table is created in the lakehouse. You can disable this behavior in the settings of the SQL analytics endpoint."

Reference: Default Power BI semantic models in Microsoft Fabric

2. Microsoft Learn. (2024). SQL analytics endpoint. "In the SQL analytics endpoint

select Settings in the ribbon. ... Sync the default Power BI semantic model: This setting allows you to automatically sync objects from the SQL analytics endpoint to the default Power BI semantic model."

Reference: SQL analytics endpoint

To create a calculation item that modifies the filter context for time intelligence (like Month-to-Date), the CALCULATE function is required. It evaluates an expression in a modified filter context. Within a calculation group, the specific measure being evaluated is dynamic; therefore, the SELECTEDMEASURE function is used as a reference to the measure that is currently in context for the visual.

The standard pattern for such a calculation item is:

CALCULATE (SELECTEDMEASURE(), DATESMTD('Date' [Date]))

GENERATE creates tables, and MEASURE is not a valid function for this context. SELECTEDVALUE returns a single value from a column, not the dynamic measure reference required here.

Microsoft Learn. "Calculation groups - DAX." Data Analysis Expressions (DAX) Reference. Accessed January 20, 2026. https://learn.microsoft.com/en-us/dax/calculation-groups-dax

Microsoft Learn. "SELECTEDMEASURE function (DAX)." Data Analysis Expressions (DAX) Reference. Accessed January 20, 2026. https://learn.microsoft.com/en-us/dax/selectedmeasure-function-dax

Microsoft Learn. "CALCULATE function (DAX)." Data Analysis Expressions (DAX) Reference. Accessed January 20, 2026. https://learn.microsoft.com/en-us/dax/calculate-function-dax

The Script activity in a Microsoft Fabric pipeline is designed to execute one or more SQL statements, including stored procedures, against a supported data store like a Fabric warehouse. A key feature of the Script activity is its ability to return a result set or output values from the executed script. This output can then be consumed by subsequent, downstream activities within the same pipeline, directly fulfilling the requirement to make the returned values available for further processing.

B. Copy data: The primary purpose of the Copy data activity is to move data between data stores. It is not designed for executing a procedure solely to capture its output for pipeline control flow.

C. Switch: The Switch activity is a control flow construct that directs the pipeline's execution path based on an expression's value. It does not execute external scripts or stored procedures.

D. Append variable: This activity is used to add a value to an existing array variable within the pipeline's context. It does not have the capability to execute a stored procedure.

---

1. Microsoft Learn. (2024). Script activity in Microsoft Fabric.

Reference: In the "Overview" section

it states

"The Script activity allows you to execute a block of commands against a supported data store... You can also pass parameters into your script and return results back to the pipeline." This confirms its ability to execute scripts (like a stored procedure call) and make the results available downstream.

2. Microsoft Learn. (2024). Control flow activities in a Data Factory pipeline in Microsoft Fabric.

Reference: This document provides an overview of control flow activities. It describes the Switch activity as a conditional that "provides the same functionality that a switch statement provides in programming languages

" and the Append Variable activity as one that "add[s] a value to an existing array variable." Neither description includes executing external code like a stored procedure.

The "Large semantic model storage format" is a workspace-level setting in Microsoft Fabric and Power BI Premium that optimizes storage for large and complex models. This format is specifically engineered to improve the performance of write operations. Deploying a semantic model via the XMLA endpoint is a write-intensive operation. By enabling this setting, the service uses a different storage architecture that reduces fragmentation and speeds up write operations like deployments, refreshes, and commits. This directly addresses the requirement to minimize the deployment time for Model1.

A. Select Small semantic model storage format.

This is the default format, optimized for interactive authoring and querying, not for the write-intensive operations involved in deploying large models, making it less efficient for this task.

B. Select Users can edit data models in the Power Bl service.

This setting relates to user permissions and enabling web-based data modeling. It has no impact on the underlying storage architecture or the performance of deployments via the XMLA endpoint.

D. Set Enable Cache for Shortcuts to On.

This setting improves query performance by caching data from OneLake shortcuts. It does not affect the speed of deploying the semantic model's metadata and structure.

1. Microsoft Learn. (2024). Large semantic models. Microsoft Docs.

Reference: In the section "Enable large semantic model storage format

" the documentation states

"When enabled

the semantic model storage format is set to support very large semantic models with optimizations for write operations." This confirms that the setting is designed to speed up operations like deployment.

2. Microsoft Learn. (2024). Workspace settings. Microsoft Docs.

Reference: Under the "Power BI > General" section for workspace settings

the "Large semantic model storage format" is described. It explains that this setting must be enabled before the model is published to the workspace to take effect

reinforcing its role in the deployment process.

To view column statistics such as minimum and maximum values in the Power Query editor, you must use the data profiling tools. The primary step is to select Enable column profile (E) from the View tab. This master option activates the data profiling features.

The specific information, including min and max values, is presented within the Show column value distribution (D) view. For a comprehensive analysis of a selected column, the full profile, which includes these statistics, is displayed in a dedicated pane at the bottom of the editor, which is described by Show column profile in details pane (B). Together, these three options enable the feature, identify the correct data view, and specify where to find the detailed results.

A. Show column quality details: This option only displays the percentage of valid, error, and empty values, not statistical measures like min or max.

C. Enable details pane: There is no generic "Enable details pane" option. The pane for column profiling appears contextually when the feature is enabled.

1. Microsoft Learn: Use the data profiling tools. This document details the data profiling features in Power Query.

Section: Column profile: This section explains that enabling the column profile provides an in-depth look at the data. A screenshot clearly shows the "Column statistics" pane with "min" and "max" values listed. This supports options B and E.

Section: Column distribution: This section describes the visual that appears under column headers

which includes key statistics. The accompanying screenshot confirms that "Min" and "Max" values are displayed in this view

supporting option D.

Section: Column quality

column distribution

and column profile: This introductory section shows the three main checkboxes in the "Data Preview" group on the "View" tab

confirming that "Column profile" and "Column distribution" are distinct selectable options. This validates the selection of D and E.

For Box 1, the goal is to calculate the arithmetic mean of the satisfaction scores. The AVERAGE function is the standard aggregation function for this purpose, applied to the column containing the numerical scores ('Survey'[Response Value]). The AVERAGEX option provided is syntactically incomplete (missing the expression argument) and conceptually incorrect for a simple column average.

For Box 2, the CALCULATE function requires filter arguments to define the calculation context. The variable Period is defined earlier in the code using the DATESINPERIOD function, which returns a table of dates corresponding to the rolling 12-month window. In DAX, passing a table expression (like Period) into CALCULATE applies that table as a filter. The other options (NumberOfMonths, LastCurrentDate) are scalar values, which cannot be used directly as standalone filter arguments in CALCULATE.

Microsoft. (2024). CALCULATE function (DAX). Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/dax/calculate-function-dax

Microsoft. (2024). DATESINPERIOD function (DAX). Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/dax/datesinperiod-function-dax

Microsoft. (2024). AVERAGE function (DAX). Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/dax/average-function-dax

Dropdown 1 (CALCULATE): The CALCULATE function is required to evaluate the [Total Sales] measure in a modified filter context. The SAMEPERIODLASTYEAR function returns a table of dates shifted one year back, which acts as the filter. Other options like SUMMARIZE or CALCULATETABLE require a table as the first argument, whereas [Total Sales] is a scalar measure.

Dropdown 2 ({ _LYSales }): The EVALUATE statement in a DAX query requires a table expression as its output. Since the variable _LYSales (derived from CALCULATE) stores a scalar value (a single number), it cannot be evaluated directly. The table constructor syntax { _LYSales } (curly braces) is used to convert this scalar value into a single-row, single-column table.

Microsoft. (n.d.). CALCULATE function (DAX). Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/dax/calculate-function-dax

Microsoft. (n.d.). EVALUATE statement (DAX). Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/dax/dax-queries#evaluate

Microsoft. (n.d.). Table Constructor. Microsoft Learn. Retrieved from https://learn.microsoft.com/en-us/dax/table-constructor-dax

To address the performance issues and storage cleanup in the Delta table:

1. Remove the files: The VACUUM command is used in Delta Lake to physically remove data files that are no longer referenced by the transaction log and are older than the retention threshold (default 7 days). This clears out the "data and log files that are no longer used."
2. Combine the files: The OPTIMIZE command compacts small files (bin-packing) into larger ones to improve read performance. The default target file size for OPTIMIZE is 1 GB, which aligns exactly with the requirement. While autoCompact also combines files, it typically targets smaller file sizes (128 MB) and occurs during writes, whereas OPTIMIZE is best run on a schedule to reorganize existing fragmented data into 1 GB files.

Microsoft Learn - Optimize data file layout: "The OPTIMIZE command compacts small files into larger ones... The target file size for the OPTIMIZE operation defaults to 1 GB."

Source: Optimize data file layout - Azure Databricks | Microsoft Learn

Microsoft Learn - Remove unused data files with vacuum: "You can remove files no longer referenced by a Delta table and are older than the retention threshold by running the VACUUM command on the table."

Source: Remove unused data files with vacuum - Azure Databricks | Microsoft Learn