Microsoft Implementing Analytics Solutions Using Microsoft Fabric (DP-600 Korean Version) - DP-600 Korean Exam Practice Test

Explanation:

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Comprehensive Detailed Explanation
We need to write a query that summarizes order quantities by year and product, and also include the yearly total for all products in each row.
Step 1: What the query needs
Extract the year from SalesOrderDetail.ModifiedDate.
Join with the Product table to get the product name.
Aggregate with SUM(OrderQty).
Return grouped data by year and product, with an additional subtotal row per year (all products combined).
Step 2: Evaluate the SELECT Clause
We must extract the year using:
YEAR(so.ModifiedDate) AS OrderDate
This converts the ModifiedDate column into a year value for grouping.
Step 3: Evaluate the GROUP BY options
CUBE(YEAR, P.Name) # Produces all combinations of year totals, product totals, and grand totals. Too many combinations, not required.
GROUPING SETS # Could achieve the result but requires explicitly listing the sets. Less direct.
ROLLUP(YEAR, P.Name) # Produces grouping by (Year, Product) and then a subtotal per Year. Exactly what is required.
YEAR only # Would group only by year, losing per-product breakdown.
Correct: ROLLUP(YEAR(so.ModifiedDate), P.Name)
Step 4: Completed Query
SELECT
YEAR(so.ModifiedDate) AS OrderDate,
p.Name,
SUM(so.OrderQty) AS OrderQty
FROM dbo.SalesOrderDetail so
INNER JOIN dbo.Product p
ON p.ProductID = so.ProductID
GROUP BY ROLLUP(YEAR(so.ModifiedDate), p.Name);
Why This Works
YEAR(so.ModifiedDate) extracts year for grouping.
ROLLUP(YEAR, P.Name) provides both product-level totals and yearly subtotals.
Ensures the requirement: "include the yearly sum of order quantities for all the products in each row." References GROUP BY ROLLUP in T-SQL Aggregate functions in Microsoft Fabric warehouses

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When creating a shortcut to an Azure Data Lake Storage Gen2 account in a lakehouse, you should use the abfss (Azure Blob File System Secure) connection string and the dfs (Data Lake File System) endpoint. The abfss is used for secure access to Azure Data Lake Storage, and the dfs endpoint indicates that the Data Lake Storage Gen2 capabilities are to be used.

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Step 1 - Read the Parquet file into a DataFrame
df = spark.read.parquet( " abfss://[email protected]/files/sales.parquet " ) This correctly loads the Parquet data into Spark.
Step 2 - Write into the Lakehouse as a managed table
If we want the result to be registered as a Lakehouse table and automatically appear in Lakehouse Explorer, we must:
Write the data in delta format (because Fabric Lakehouse tables are Delta tables).
Save the table under the tables folder, not files.
So the correct code is:
df.write.mode( " overwrite " ).format( " delta " ).saveAsTable( " tables/sales " ) Final Answer:
Format: delta
SaveAsTable Path: tables/sales
References:
Lakehouse tables in Microsoft Fabric
Save DataFrame as Delta Table in Spark
# Answer Selection:
First dropdown # delta
Second dropdown # tables/sales

Explanation:
The Direct Lake fallback behavior is set to: DirectQueryOnly
The query for the table visual is executed by using: DirectQuery
In the context of Microsoft Power BI, when using DirectQuery in Direct Lake mode, there is no caching of data and all queries are sent directly to the underlying data source. The Performance Analyzer tool shows the time taken for different operations, and from the options provided, it indicates that DirectQuery mode is being used for the visuals, which is consistent with the Direct Lake setting. DirectQueryOnly as the fallback behavior ensures that only DirectQuery will be used without reverting to import mode.

Explanation:

Step 1: Requirement
We need to calculate total sales amount by OrderDate.
Total Sales Amount = Quantity × UnitPrice per row.
Then SUM over all rows per OrderDate.
Only include rows where the OrderDate matches a public holiday in Australia.
Step 2: Calculation Logic
If we select s.Quantity * s.UnitPrice, it only computes row-level values.
To calculate the total sales amount per OrderDate, we need:
SUM(s.Quantity * s.UnitPrice) AS TotalSalesAmt
Thus, the correct calculation: SUM(s.Quantity * s.UnitPrice)
Step 3: Join Type
We want to return only those sales that occurred on public holidays in Australia.
If we used a LEFT JOIN, we'd include all sales regardless of holiday.
A FULL OUTER JOIN would include non-matching rows, not required.
A CROSS JOIN would duplicate results unnecessarily.
A RIGHT OUTER JOIN would include holidays with no sales, which is not needed.
The correct choice is an INNER JOIN on matching dates.
Step 4: Final Query
SELECT
s.OrderDate,
SUM(s.Quantity * s.UnitPrice) AS TotalSalesAmt
FROM dbo.sales AS s
INNER JOIN dbo.publicholidays AS ph
ON s.OrderDate = ph.Date
WHERE ph.countryOrRegion = ' Australia '
GROUP BY s.OrderDate;
Why This is Correct
SUM(s.Quantity * s.UnitPrice) # ensures sales are aggregated correctly.
INNER JOIN # ensures only rows where OrderDate matches a holiday are included.
WHERE ph.countryOrRegion = ' Australia ' # filters holidays to Australia only.
References
SUM (Transact-SQL)
JOIN operations in T-SQL