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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q121-Q126):

NEW QUESTION # 121
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 # 122
You need to create a Microsoft Power BI file that will be used to create multiple reports. The solution must meet the following requirements:
- The file must include predefined data source connections.
- The file must include the report structure and formatting.
- The file must NOT contain any data.
Which file format should you use?

A. PBIP
B. PBIT
C. PBIDS
D. PBIX

Answer: B

NEW QUESTION # 123
Which type of data store should you recommend in the AnalyticsPOC workspace?

A. an external Hive metaStore
B. a warehouse
C. a data lake
D. a lakehouse

Answer: D

Explanation:
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.

NEW QUESTION # 124
You have a Fabric tenant that uses a Microsoft Power BI Premium capacity.
You need to enable scale-out for a semantic model.
What should you do first?

A. At the semantic model level, set Large dataset storage format to Off.
B. At the tenant level, set Create and use Metrics to Enabled.
C. At the semantic model level, set Large dataset storage format to On.
D. At the tenant level, set Data Activator to Enabled.

Answer: C

Explanation:
https://learn.microsoft.com/en-us/power-bi/enterprise/service-premium-scale-out-configure

NEW QUESTION # 125
Hotspot Question
You have a Fabric tenant that contains a workspace named Workspace1. Workspace1 contains a warehouse named DW1. DW1 contains two tables named Employees and Sales. All users have read access to Dw1.
You need to implement access controls to meet the following requirements:
- For the Sales table, ensure that the users can see only the sales
data from their respective region.
- For the Employees table, restrict access to all Personally
Identifiable Information (PII).
- Maintain access to unrestricted data for all the users.
What should you use for each table? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 126
......

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