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Most Effective Way to Get Microsoft DP-600 Certification

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Microsoft DP-600 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

Implement and manage semantic models: The topic delves into designing and building semantic models, and optimizing enterprise-scale semantic models.

Topic 3

Prepare data: In this topic, questions about creating objects in a lakehouse or warehouse, copying data, transforming data, and optimizing performance appear.

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The Microsoft DP-600 exam questions are being offered in three different formats. These formats are Implementing Analytics Solutions Using Microsoft Fabric (DP-600) PDF dumps files, desktop practice test software, and web-based practice test software. All these three Implementing Analytics Solutions Using Microsoft Fabric (DP-600) exam dumps formats contain the real Implementing Analytics Solutions Using Microsoft Fabric (DP-600) exam questions that assist you in your Implementing Analytics Solutions Using Microsoft Fabric (DP-600) practice exam preparation and finally, you will be confident to pass the final DP-600 exam easily.

Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q31-Q36):

NEW QUESTION # 31
You have source data in a folder on a local computer.
You need to create a solution that will use Fabric to populate a data store. The solution must meet the following requirements:
* Support the use of dataflows to load and append data to the data store.
* Ensure that Delta tables are V-Order optimized and compacted automatically.
Which type of data store should you use?

A. an Azure SQL database
B. a warehouse
C. a KQL database
D. a lakehouse

Answer: D

Explanation:
A lakehouse (A) is the type of data store you should use. It supports dataflows to load and append data and ensures that Delta tables are Z-Order optimized and compacted automatically. References = The capabilities of a lakehouse and its support for Delta tables are described in the lakehouse and Delta table documentation.

NEW QUESTION # 32
You have a Microsoft Fabric tenant that contains a dataflow.
You are exploring a new semantic model.
From Power Query, you need to view column information as shown in the following exhibit.

Which three Data view options should you select? Each correct answer presents part of the solution. NOTE: Each correct answer is worth one point.

A. Show column profile in details pane
B. Enable column profile
C. Enable details pane
D. Show column quality details
E. Show column value distribution

Answer: B,C,D

Explanation:
To view column information like the one shown in the exhibit in Power Query, you need to select the options that enable profiling and display quality and distribution details.
These are: A. Enable column profile - This option turns on profiling for each column, showing statistics such as distinct and unique values. B. Show column quality details - It displays the column quality bar on top of each column showing the percentage of valid, error, and empty values. E. Show column value distribution - It enables the histogram display of value distribution for each column, which visualizes how often each value occurs.

NEW QUESTION # 33
You have a Fabric tenant tha1 contains a takehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.
When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.
You need to identify whether maintenance tasks were performed on Customer.
Solution: You run the following Spark SQL statement:
EXPLAIN TABLE customer
Does this meet the goal?

A. No
B. Yes

Answer: A

Explanation:
No, the EXPLAIN TABLE statement does not identify whether maintenance tasks were performed on a table. It shows the execution plan for a query. Reference = The usage and output of the EXPLAIN command can be found in the Spark SQL documentation.

NEW QUESTION # 34
You have a Fabric warehouse that contains a table named Staging.Sales. Staging.Sales contains the following columns.

You need to write a T-SQL query that will return data for the year 2023 that displays ProductID and ProductName and has a summarized Amount that is higher than 10,000.
Which query should you use?

Answer: A

NEW QUESTION # 35
You have a Fabric tenant that contains a semantic model. The model contains data about retail stores.
You need to write a DAX query that will be executed by using the XMLA endpoint The query must return a table of stores that have opened since December 1,2023.
How should you complete the DAX expression? To answer, drag the appropriate values to the correct targets.
Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
The correct order for the DAX expression would be:
* DEFINE VAR _SalesSince = DATE ( 2023, 12, 01 )
* EVALUATE
* FILTER (
* SUMMARIZE ( Store, Store[Name], Store[OpenDate] ),
* Store[OpenDate] >= _SalesSince )
In this DAX query, you're defining a variable _SalesSince to hold the date from which you want to filter the stores. EVALUATE starts the definition of the query. The FILTER function is used to return a table that filters another table or expression. SUMMARIZE creates a summary table for the stores, including the Store[Name] and Store[OpenDate] columns, and the filter expression Store[OpenDate] >= _SalesSince ensures only stores opened on or after December 1, 2023, are included in the results.
References =
* DAX FILTER Function
* DAX SUMMARIZE Function

NEW QUESTION # 36
......

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