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| Section | Objectives |
|---|---|
| Implement data engineering solutions | - Lakehouse architecture
|
| Implement analytics solutions | - Data warehousing and modeling
|
| Real-time analytics | - Streaming and event processing
|
| Manage and secure Microsoft Fabric | - Governance and administration
|
| Get data into Microsoft Fabric | - Data ingestion and integration
|
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NEW QUESTION # 185
You have a Fabric tenant that contains a warehouse named WH1. You run the following T-SQL query against WH1.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
We are analyzing the query:
SELECT e.[WWI Employee ID],
e.Employee,
e.[Preferred Name],
gdr.[WWI Employee ID] AS [Direct Report ID],
gdr.Employee AS [Direct Report]
FROM Dimension.Employee AS e
OUTER APPLY Dimension.GetDirectReports(e.[Employee Key]) AS gdr;
Key concepts:
APPLY operator: Used to join a table with a table-valued function.
OUTER APPLY ensures all rows from the left (Employee) are returned, even if the function returns no rows (similar to LEFT JOIN).
Table-valued function (TVF): Returns a set of rows (unlike scalar functions, which return a single value).
The function executes once per row from the left table, not once for the whole query.
Evaluating each statement:
"Dimension.GetDirectReports is a scalar T-SQL function."
No # It must be a table-valued function, because OUTER APPLY requires a table expression.
"The Dimension.GetDirectReports function will run only once when the query runs." No # With APPLY, the function runs for each row of Dimension.Employee.
"The output rows will include at least one row for each row in the Dimension.Employee table." Yes # Because of OUTER APPLY, every employee row is included, even if there are no direct reports (in that case, the columns from gdr will be NULL).
Final Answer:
Scalar function # No
Runs once per query # No
At least one row per Employee # Yes
References:
APPLY operator in T-SQL
Table-valued functions
NEW QUESTION # 186
You are analyzing customer purchases in a Fabric notebook by using PySpanc You have the following DataFrames:
You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling. You write the following code.
Which code should you run to populate the results DataFrame?
Answer: A
Explanation:
The correct code to populate the results DataFrame with minimal data shuffling is Option A.
Using the broadcast function in PySpark is a way to minimize data movement by broadcasting the smaller DataFrame ( customers) to each node in the cluster.
This is ideal when one DataFrame is much smaller than the other, as in this case with customers.
References = You can refer to the official Apache Spark documentation for more details on joins and the broadcast hint.
NEW QUESTION # 187
You have a Fabric tenant that contains a lakehouse named lakehouse1. Lakehouse1 contains a table named Table1.
You are creating a new data pipeline.
You plan to copy external data to Table1. The schema of the external data changes regularly.
You need the copy operation to meet the following requirements:
* Replace Table1 with the schema of the external data.
* Replace all the data in Table1 with the rows in the external data.
You add a Copy data activity to the pipeline. What should you do for the Copy data activity?
Answer: C
NEW QUESTION # 188
You have a Fabric warehouse that contains two tables named DimDate and Trips.
DimDate contains the following fields.
Trips contains the following fields.
You need to compare the average miles per trip for statutory holidays versus non-statutory holidays.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Comprehensive Detailed Explanation
Step 1: Requirement
We need to compare the average miles per trip for:
Statutory holidays (when IsHoliday = 1)
Non-statutory holidays (when IsHoliday = 0)
Step 2: Formula for average miles per trip
Average miles per trip = total miles ÷ number of trips
Total miles # SUM(t.tripDistance)
Number of trips # COUNT(t.tripID)
So the calculation is:
(SUM(t.tripDistance) / COUNT(t.tripID)) AS MilesPerTrip
Step 3: Grouping
We need a comparison by holiday status.
So we must group the results by:
GROUP BY d.IsHoliday
This ensures we get two rows: one for IsHoliday = 1 and one for IsHoliday = 0.
Step 4: Final Query
SELECT
d.IsHoliday,
(SUM(t.tripDistance) / COUNT(t.tripID)) AS MilesPerTrip
FROM DimDate d
INNER JOIN Trips t ON d.DateID = t.DateID
GROUP BY d.IsHoliday;
Why This is Correct
The formula ensures average miles per trip.
Grouping ensures comparison between holidays vs non-holidays.
Efficient aggregation, minimal computation.
References
Aggregate functions in T-SQL
GROUP BY clause
NEW QUESTION # 189
You need to design a semantic model for the customer satisfaction report.
Which data source authentication method and mode should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
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
NEW QUESTION # 190
......
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