Pass the Microsoft Microsoft Certified: Fabric Analytics Engineer Associate DP-600 Questions and answers with CertsForce

The results will form a hierarchy of folders for each partition key. - Yes

The resulting file partitions can be read in parallel across multiple nodes. - Yes

The resulting file partitions will use file compression. - No

Partitioning data by columns such as year, month, and day, as shown in the DataFrame write operation, organizes the output into a directory hierarchy that reflects the partitioning structure. This organization can improve the performance of read operations, as queries that filter by the partitioned columns can scan only the relevant directories. Moreover, partitioning facilitates parallelism because each partition can be processed independently across different nodes in a distributed system like Spark. However, the code snippet provided does not explicitly specify that file compression should be used, so we cannot assume that the output will be compressed without additional context.

References =

DataFrame write partitionBy

Apache Spark optimization with partitioning

Comprehensive Detailed Explanation

We need to create a computed column PriceRange based on the UnitPrice column in the dbo.Product table.

Step 1: Requirements

If UnitPrice = 0 → "Not for resale"

If UnitPrice < 50 → "Under $50"

If UnitPrice >= 50 AND UnitPrice < 250 → "Under $250"

Otherwise → "$250+"

This matches a CASE expression in T-SQL.

Step 2: CASE Expression Structure

The syntax is:

CASE

WHEN condition THEN result

WHEN condition THEN result

ELSE result

END

Step 3: Apply to Problem

SELECT

Item,

UnitPrice,

PriceRange = CASE

WHEN UnitPrice = 0 THEN 'Not for resale'

WHEN UnitPrice < 50 THEN 'Under $50'

WHEN UnitPrice >= 50 AND UnitPrice < 250 THEN 'Under $250'

ELSE '$250+'

END

FROM [Warehouse1].[dbo].[Product];

Step 4: Why This is Correct

CASE starts the conditional evaluation.

ELSE handles the default branch ($250+).

END closes the expression.

Meets all requirements exactly.

References

CASE expression in T-SQL

Computed columns in T-SQL

DF1 will be deployed to Workspace_TEST: Yes

Data from Model1 will be deployed to Workspace_TEST: No

The scheduled refresh policy for Model1 will be deployed to Workspace_TEST: No

DF1 is a Dataflow Gen1 with a configured scheduled refresh policy, so it will be deployed to Workspace_TEST.

Data from Model1 will not be deployed as Model1's scheduled refresh policy is not configured, indicating it is not set up for deployment.

The scheduled refresh policy for Model1 will not be deployed since it is listed as "Not configured."

A possible cause for the failure is that query folding is NOT occurring (D). Query folding helps optimize refresh by pushing down the query logic to the source system, reducing the amount of data processed and transferred, hence conserving resources. References = The Power BI documentation on incremental refresh and query folding provides detailed information on this topic.

To implement row-level security (RLS) in a Fabric Warehouse (like in Azure Synapse or SQL Server):

You must define a predicate function (usually an inline table-valued function) that filters rows for each user → Function.

Then you bind that function to the table using a Security Policy → Security Policy.

A Database Role is used for group-based access control but not specifically for implementing RLS.

Stored procedures and constraints are not used for RLS.

Correct answers: A and D

The Spark engine will read only the 'SalesOrderNumber', 'OrderDate', 'CustomerName', 'UnitPrice' columns from Sales_raw.csv. - Yes

Removing the partition will reduce the execution time of the query. - No

Adding inferSchema='true' to the options will increase the execution time of the query. - Yes

The code specifies the selection of certain columns, which means only those columns will be read into the DataFrame. Partitions in Spark are a way to optimize the execution of queries by organizing the data into parts that can be processed in parallel. Removing the partition could potentially increase the execution time because Spark would no longer be able to process the data in parallel efficiently. The inferSchema option allows Spark to automatically detect the column data types, which can increase the execution time of the initial read operation because it requires Spark to read through the data to infer the schema.

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).

The measure should use the AVERAGE function to calculate the average value.

It should reference the Response Value column from the 'Survey' table.

The 'Number of months' should be used to define the period for the average calculation.

To calculate the average overall satisfaction score using DAX, you would need to use the AVERAGE function on the response values related to satisfaction questions. The DATESINPERIOD function will help in calculating the rolling average over the last 12 months.

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.

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.