Free PDF DP-600 - Implementing Analytics Solutions Using Microsoft Fabric–Valid Free Exam

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Microsoft DP-600 Exam Syllabus Topics:

Topic	Details
Topic 1	Explore and analyze data: It also deals with performing exploratory analytics. Moreover, the topic delves into query data by using SQL.
Topic 2	Implement and manage semantic models: The topic delves into designing and building semantic models, and optimizing enterprise-scale semantic models.
Topic 3	Plan, implement, and manage a solution for data analytics: Planning a data analytics environment, implementing and managing a data analytics environment are discussed in this topic. It also focuses on managing the analytics development lifecycle.
Topic 4	Prepare and serve 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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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q61-Q66):

NEW QUESTION # 61
You need to create a data loading pattern for a Type 1 slowly changing dimension (SCD).
Which two actions should you include in the process? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.

A. Update the effective end date of rows when the non-key attribute values have changed.
B. Update rows when the non-key attributes have changed.
C. Insert new records when the natural key is a new value in the table.
D. Insert new rows when the natural key exists in the dimension table, and the non-key attribute values have changed.

Answer: B,C

Explanation:
For a Type 1 SCD, you should include actions that update rows when non-key attributes have changed (A), and insert new records when the natural key is a new value in the table (D). A Type 1 SCD does not track historical data, so you always overwrite the old data with the new data for a given key. Reference = Details on Type 1 slowly changing dimension patterns can be found in data warehousing literature and Microsoft's official documentation.

NEW QUESTION # 62
You have a Fabric workspace that uses the default Spark starter pool and runtime version 1,2.
You plan to read a CSV file named Sales.raw.csv in a lakehouse, select columns, and save the data as a Delta table to the managed area of the lakehouse. Sales_raw.csv contains 12 columns.
You have 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:

NEW QUESTION # 63
You are analyzing the data in a Fabric notebook.
You have a Spark DataFrame assigned to a variable named df.
You need to use the Chart view in the notebook to explore the data manually.
Which function should you run to make the data available in the Chart view?

A. write
B. displayMTML
C. show
D. display

Answer: D

Explanation:
The display function is the correct choice to make the data available in the Chart view within a Fabric notebook. This function is used to visualize Spark DataFrames in various formats including charts and graphs directly within the notebook environment. References = Further explanation of the display function can be found in the official documentation on Azure Synapse Analytics notebooks.

NEW QUESTION # 64
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. XMLA Endpoint is set to Read Only.
B. Query folding is occurring.
C. Query folding is NOT occurring.
D. The data type of the column used to partition the data has changed.
E. Only refresh complete days is selected.

Answer: C

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. Reference = The Power BI documentation on incremental refresh and query folding provides detailed information on this topic.

NEW QUESTION # 65
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:
REFRESH TABLE customer
Does this meet the goal?

A. No
B. Yes

Answer: A

Explanation:
No, the REFRESH TABLE statement does not provide information on whether maintenance tasks were performed. It only updates the metadata of a table to reflect any changes on the data files. References = The use and effects of the REFRESH TABLE command are explained in the Spark SQL documentation.

NEW QUESTION # 66
......

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Free PDF DP-600 - Implementing Analytics Solutions Using Microsoft Fabric–Valid Free Exam

Microsoft DP-600 Exam Syllabus Topics:

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q61-Q66):

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