100% PASS MICROSOFT DP-600 - MARVELOUS ONLINE IMPLEMENTING ANALYTICS SOLUTIONS USING MICROSOFT FABRIC LAB SIMULATION

100% Pass Microsoft DP-600 - Marvelous Online Implementing Analytics Solutions Using Microsoft Fabric Lab Simulation

100% Pass Microsoft DP-600 - Marvelous Online Implementing Analytics Solutions Using Microsoft Fabric Lab Simulation

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Microsoft DP-600 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Prepare data: In this topic, questions about creating objects in a lakehouse or warehouse, copying data, transforming data, and optimizing performance appear.
Topic 2
  • Maintain a data analytics solution: This section is all about implementing security and governance. In this topic, you also get information about maintaining the analytics development lifecycle.
Topic 3
  • Implement and manage semantic models: The topic delves into designing and building semantic models, and optimizing enterprise-scale semantic models.

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q82-Q87):

NEW QUESTION # 82
You have a Fabric tenant.
You plan to create a Fabric notebook that will use Spark DataFrames to generate Microsoft Power Bl visuals.
You run the following code.

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:
* The code embeds an existing Power BI report. - No
* The code creates a Power BI report. - Yes
* The code displays a summary of the DataFrame. - Yes
The code provided seems to be a snippet from a SQL query or script which is neither creating nor embedding a Power BI report directly. It appears to be setting up a DataFrame for use within a larger context, potentially for visualization in Power BI, but the code itself does not perform the creation or embedding of a report. Instead, it's likely part of a data processing step that summarizes data.
References =
* Introduction to DataFrames - Spark SQL
* Power BI and Azure Databricks


NEW QUESTION # 83
You have the source data model shown in the following exhibit.

The primary keys of the tables are indicated by a key symbol beside the columns involved in each key.
You need to create a dimensional data model that will enable the analysis of order items by date, product, and customer.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

* The relationship between OrderItem and Product must be based on: Both the CompanyID and the ProductID columns
* The Company entity must be: Denormalized into the Customer and Product entities In a dimensional model, the relationships are typically based on foreign key constraints between the fact table (OrderItem) and dimension tables (Product, Customer, Date). Since CompanyID is present in both the OrderItem and Product tables, it acts as a foreign key in the relationship. Similarly, ProductID is a foreign key that relates these two tables. To enable analysis by date, product, and customer, the Company entity would need to be denormalized into the Customer and Product entities to ensure that the relevant company information is available within those dimensions for querying and reporting purposes.
References =
* Dimensional modeling
* Star schema design


NEW QUESTION # 84
You have a Fabric tenant that contains a warehouse named DW1 and a lakehouse named LH1.
DW1 contains a table named Sales.Product. LH1 contains a table named Sales.Orders.
You plan to schedule an automated process that will create a new point-in-time (PIT) table named Sales.ProductOrder in DW1. Sales.ProductOrder will be built by using the results of a query that will join Sales.Product and Sales.Orders.
You need to ensure that the types of columns in Sales.ProductOrder match the column types in the source tables. The solution must minimize the number of operations required to create the new table.
Which operation should you use?

  • A. CREATE TABLE AS CLONE OF
  • B. INSERT INTO
  • C. CREATE TABLE AS SELECT (CTAS)
  • D. CREATE MATERIALIZED VIEW AS SELECT

Answer: C

Explanation:
The CREATE TABLE AS SELECT (CTAS) statement allows you to create a new table directly from the results of a query, including joining tables. It automatically determines the column types based on the query's result set, ensuring they match the source tables. This minimizes the number of operations required.


NEW QUESTION # 85
You have a Fabric tenant named Tenant1 that contains a lakehouse named Lakehouse1.
You need to add data to Lakehouse1 from a CSV file in an Azure Storage account outside of Fabric. The solution must minimize development effort.
What should you use to add the data?

  • A. pipeline
  • B. shortcut
  • C. Dataflow Gen2
  • D. copy job

Answer: B


NEW QUESTION # 86
You have a Fabric tenant that contains 30 CSV files in OneLake. The files are updated daily.
You create a Microsoft Power Bl semantic model named Modell that uses the CSV files as a data source. You configure incremental refresh for Model 1 and publish the model to a Premium capacity in the Fabric tenant.
When you initiate a refresh of Model1, the refresh fails after running out of resources.
What is a possible cause of the failure?

  • A. Query folding is NOT occurring.
  • B. Query folding is occurring.
  • C. XMLA Endpoint is set to Read Only.
  • D. The data type of the column used to partition the data has changed.
  • E. Only refresh complete days is selected.

Answer: A

Explanation:
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.


NEW QUESTION # 87
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