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.

The code embeds an existing Power BI report. - No

The code creates a Power BI report. - Yes

The code displays a summary of the DataFrame. - Yes

The code provided seems to be a snippet from a SQL query or script which is neither creating nor embedding a Power BI report directly. It appears to be setting up a DataFrame for use within a larger context, potentially for visualization in Power BI, but the code itself does not perform the creation or embedding of a report. Instead, it ' s likely part of a data processing step that summarizes data.

References =

Introduction to DataFrames - Spark SQL

Power BI and Azure Databricks

Supports complex calculated columns (with aggregate functions, calculated tables, MDX hierarchies).

Minimizes page rendering times.

Step 1 – Choosing the Mode

Direct Lake → Best for near real-time queries, avoids duplication, but has limitations (e.g., some complex calculated columns, MDX user hierarchies are not fully supported).

DirectQuery → Sends queries to the source each time. It supports complex expressions but is slow (not optimal for minimizing page rendering times).

Import → Data is loaded into VertiPaq in-memory engine, supports full DAX capabilities, calculated tables, MDX hierarchies, and provides fastest query performance.

→ Correct choice: Import.

Step 2 – Choosing Query Caching

Capacity default → Relies on the workspace/capacity setting.

Off → Disables caching, which could slow down report rendering.

On → Ensures queries are cached for faster page rendering times.

→ Correct choice: On.

Final Answer:

Mode: Import

Query Caching: On

For the HighestSellingPrice, you should use the GREATEST function to find the highest value from the given price columns. However, T-SQL does not have a GREATEST function as found in some other SQL dialects, so you would typically use a CASE statement or an IIF statement with nested MAX functions. Since neither of those are provided in the options, you should select MAX as a placeholder to indicate the function that would be used to find the highest value if combining multiple MAX functions or a similar logic was available.

For the TradePrice, you should use the COALESCE function, which returns the first non-null value in a list. The COALESCE function is the correct choice as it will return AgentPrice if it ' s not null; if AgentPrice is null, it will check WholesalePrice, and if that is also null, it will return ListPrice.

The complete code with the correct SQL functions would look like this:

SELECT ProductID,

MAX(ListPrice, WholesalePrice, AgentPrice) AS HighestSellingPrice, -- MAX is used as a placeholder

COALESCE(AgentPrice, WholesalePrice, ListPrice) AS TradePrice

FROM Sales.Products

Select MAX for HighestSellingPrice and COALESCE for TradePrice in the answer area.

PBIR-Legacy format → No

Located in Power BI service → No

Opens in full edit mode → Yes

Comprehensive Detailed Explanation

We are analyzing a Power BI Report (PBIR) definition file in JSON format:

{

" version " : " 1.0 " ,

" byPath " : {

" path " : " ../Sales.Dataset "

}

}

}

Step 1: Is this PBIR-Legacy?

PBIR-Legacy format is used when referencing a dataset hosted in the Power BI service (cloud).

In this case, the JSON uses byPath, which references a local dataset file (Sales.Dataset) in the project folder, not a service dataset.

Therefore: No, it is not PBIR-Legacy.

Step 2: Where is the semantic model located?

The datasetReference points to ../Sales.Dataset.

That means the semantic model is stored locally in the project structure, not in the Power BI service.

Therefore: No, it is not located in the Power BI service.

Step 3: What happens when opening in Power BI Desktop?

Since the dataset reference is local (byPath), Power BI Desktop opens the semantic model in full edit mode (allowing schema, relationships, measures, etc. to be modified).

If it were PBIR-Legacy with a service model, only Live Connection mode would be available.

Therefore: Yes, Power BI Desktop opens it in full edit mode.

References

PBIR file structure in Power BI projects

Semantic model references: byPath vs byConnection

Final Answer:

PBIR-Legacy format → No

Located in Power BI service → No

Opens in full edit mode → Yes

To convert CSV files into the delta format with Z-Order optimization enabled, you should use the Optimize feature (D) from Lakehouse Explorer. This will allow you to optimize the file organization for the most efficient querying. References = The process for converting and optimizing file formats within a lakehouse is discussed in the lakehouse management documentation.

To meet the requirements, developers should have Admin access to the deployment pipeline (B), Contributor access to the Development and Test workspaces (E), and Viewer access to the Production workspace (D). This setup ensures they can perform necessary actions in development and test environments without having the ability to affect production. References = The Power BI documentation on workspace access levels and deployment pipelines provides guidelines on assigning appropriate permissions.

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.

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.

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).