DP-600 Exam Dumps - Implementing Analytics Solutions Using Microsoft Fabric

For the PeriodDate that returns the first day of the month for OrderDate, you should use DATEFROMPARTS as it allows you to construct a date from its individual components (year, month, day).

For the DayName that returns the name of the day for OrderDate, you should use DATENAME with the weekday date part to get the full name of the weekday.

The complete SQL query should look like this:

SELECT OrderID, CustomerID,

DATEFROMPARTS(YEAR(OrderDate), MONTH(OrderDate), 1) AS PeriodDate,

DATENAME(weekday, OrderDate) AS DayName

FROM Sales.Orders

Select DATEFROMPARTS for the PeriodDate and weekday for the DayName in the answer area.

Scenario Recap

Fabric tenant → Workspace1

Contains:

Lakehouse LH1 (with table signindata in schema dho)

Warehouse DW1

Task: Create a stored procedure in DW1 that deduplicates rows in signindata.

Step 1: Stored procedure structure

In T-SQL, stored procedures begin with:

AS

BEGIN

-- logic

END

So the correct option for the first blank is BEGIN.

BEGIN DISTRIBUTED TRANSACTION is not needed because we are not spanning multiple servers or needing distributed transactions.

SET is not the right way to start the logic block.

Step 2: Deduplication logic

To remove duplicates from signindata, the query should return unique rows.

The simplest way is:

SELECT DISTINCT PersonID, FirstName, LastName

FROM dho.signindata;

Thus the correct choice for the second blank is DISTINCT.

GROUP BY could also deduplicate but is less efficient here since no aggregation is requested.

TOP 100 PERCENT WITH TIES is irrelevant.

Step 3: Final T-SQL stored procedure

CREATE PROCEDURE dbo.usp_GetPerson

AS

BEGIN

SELECT DISTINCT PersonID, FirstName, LastName

FROM dho.signindata;

END;

Why this is correct

BEGIN → correct stored procedure structure.

DISTINCT → ensures deduplication of rows from signindata.

References

CREATE PROCEDURE (Transact-SQL)

DISTINCT (Transact-SQL)

Comprehensive Detailed Explanation

We need to query a KQL database table named Readings to generate output that includes both the current reading and the previous reading values and datetime for each row.

Step 1: Understanding Requirements

The output table shows:

City, Area, MeterReading, Datetime

PrevMeterReading, PrevDatetime

The prev() function in KQL is used to access the value of a column from the previous row (based on order).

Step 2: Query Construction

We start with filtering:

Readings

| where City == "Copenhagen"

| sort by Datetime

To add columns that calculate the previous reading and previous datetime, we use extend:

| extend PrevMeterReading = prev(MeterReading), PrevDatetime = prev(Datetime)

extend creates new columns.

prev() pulls the previous row’s values.

Finally, we only want specific columns in the output. For this, we use project:

| project City, Area, MeterReading, Datetime, PrevMeterReading, PrevDatetime

Step 3: Completed Query

Readings

| where City == "Copenhagen"

| sort by Datetime

| extend PrevMeterReading = prev(MeterReading), PrevDatetime = prev(Datetime)

| project City, Area, MeterReading, Datetime, PrevMeterReading, PrevDatetime

Why This is Correct

extend adds calculated columns.

prev() gives access to the previous row.

project selects only the required columns.

This matches exactly the table shown in the question.

References

Kusto Query Language - extend operator

Kusto Query Language - project operator

prev() function in KQL

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.

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).