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Microsoft DP-600 Exam Syllabus Topics:

Topic	Details
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	Implement and manage semantic models: The topic delves into designing and building semantic models, and optimizing enterprise-scale semantic models.
Topic 3	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.

Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q189-Q194):

NEW QUESTION # 189
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 rows when the natural key exists in the dimension table, and the non-key attribute values have changed.
D. Insert new records when the natural key is a new value in the table.

Answer: B,D

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. References = Details on Type 1 slowly changing dimension patterns can be found in data warehousing literature and Microsoft's official documentation.

NEW QUESTION # 190
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a lakehouse 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:
DESCRIBE DETAIL customer
Does this meet the goal?

A. Yes
B. No

Answer: B

Explanation:
Describe Detail give general info about delta table, not the historical operations.
https://learn.microsoft.com/en-us/azure/databricks/delta/table-details

NEW QUESTION # 191
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. Only refresh complete days is selected.
D. The data type of the column used to partition the data has changed.
E. Query folding is NOT occurring.

Answer: D

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 # 192
You have a Fabric tenant that contains a Microsoft Power Bl report named Report 1. Report1 includes a Python visual. Data displayed by the visual is grouped automatically and duplicate rows are NOT displayed.
You need all rows to appear in the visual. What should you do?

A. Modify the Summarize By property for all columns.
B. Reference the columns in the Python code by index.
C. Modify the Sort Column By property for all columns.
D. Add a unique field to each row.

Answer: D

Explanation:
To ensure all rows appear in the Python visual within a Power BI report, option C, adding a unique field to each row, is the correct solution. This will prevent automatic grouping by unique values and allow for all instances of data to be represented in the visual. References = For more on Power BI Python visuals and how they handle data, please refer to the Power BI documentation.

NEW QUESTION # 193
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:

Explanation:

* 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.

NEW QUESTION # 194
......

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DP-600 New Dumps Files, DP-600 Test Questions Fee

DP-600 Study Guide: Implementing Analytics Solutions Using Microsoft Fabric & DP-600 Dumps Torrent & DP-600 Latest Dumps

Microsoft DP-600 Exam Syllabus Topics:

Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q189-Q194):

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