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Microsoft DP-600 Pass Exam | Related DP-600 Exams

The evergreen field of Microsoft is so attractive that it provides non-stop possibilities for the one who passes the Microsoft DP-600 exam. So, to be there on top of the Microsoft sector, earning the Implementing Analytics Solutions Using Microsoft Fabric (DP-600) certification is essential. Because of using outdated DP-600 study material, many candidates don't get success in the Implementing Analytics Solutions Using Microsoft Fabric (DP-600) exam and lose their resources.

Microsoft DP-600 Exam Syllabus Topics:

Topic	Details
Topic 1	Prepare data: This section of the exam measures the skills of engineers and covers essential data preparation tasks. It includes establishing data connections and discovering sources through tools like the OneLake data hub and the real-time hub. Candidates must demonstrate knowledge of selecting the appropriate storage type—lakehouse, warehouse, or eventhouse—depending on the use case. It also includes implementing OneLake integrations with Eventhouse and semantic models. The transformation part involves creating views, stored procedures, and functions, as well as enriching, merging, denormalizing, and aggregating data. Engineers are also expected to handle data quality issues like duplicates, missing values, and nulls, along with converting data types and filtering. Furthermore, querying and analyzing data using tools like SQL, KQL, and the Visual Query Editor is tested in this domain.
Topic 2	Maintain a data analytics solution: This section of the exam measures the skills of administrators and covers tasks related to enforcing security and managing the Power BI environment. It involves setting up access controls at both workspace and item levels, ensuring appropriate permissions for users and groups. Row-level, column-level, object-level, and file-level access controls are also included, alongside the application of sensitivity labels to classify data securely. This section also tests the ability to endorse Power BI items for organizational use and oversee the complete development lifecycle of analytics assets by configuring version control, managing Power BI Desktop projects, setting up deployment pipelines, assessing downstream impacts from various data assets, and handling semantic model deployments using XMLA endpoint. Reusable asset management is also a part of this domain.
Topic 3	Implement and manage semantic models: This section of the exam measures the skills of architects and focuses on designing and optimizing semantic models to support enterprise-scale analytics. It evaluates understanding of storage modes and implementing star schemas and complex relationships, such as bridge tables and many-to-many joins. Architects must write DAX-based calculations using variables, iterators, and filtering techniques. The use of calculation groups, dynamic format strings, and field parameters is included. The section also includes configuring large semantic models and designing composite models. For optimization, candidates are expected to improve report visual and DAX performance, configure Direct Lake behaviors, and implement incremental refresh strategies effectively.

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

NEW QUESTION # 31
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a table named Nyctaxi_raw. Nyctaxi_raw contains the following columns.

You create a Fabric notebook and attach it to lakehouse1.
You need to use PySpark code to transform the data. The solution must meet the following requirements:
* Add a column named pickupDate that will contain only the date portion of pickupDateTime.
* Filter the DataFrame to include only rows where fareAmount is a positive number that is less than 100.
How should you complete the code? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

* Add the pickupDate column: .withColumn("pickupDate", df["pickupDateTime"].cast("date"))
* Filter the DataFrame: .filter("fareAmount > 0 AND fareAmount < 100")
In PySpark, you can add a new column to a DataFrame using the .withColumn method, where the first argument is the new column name and the second argument is the expression to generate the content of the new column. Here, we use the .cast("date") function to extract only the date part from a timestamp. To filter the DataFrame, you use the .filter method with a condition that selects rows where fareAmount is greater than 0 and less than 100, thus ensuring only positive values less than 100 are included.

NEW QUESTION # 32
You have a Fabric tenant that contains a warehouse named Warehouse!. Warehousel contains two schemas name schemal and schema2 and a table named schemal.city.
You need to make a copy of schemal.city in schema2. The solution must minimize the copying of data.
Which T-SQL statement should you run?

A. INSERT INTO schema2.city SELECT * FROM schemal.city;
B. CREATE TABLE schema2.city AS CLONE OF schemal.city;
C. CREATE TABLE schema2.city AS SELECT * FROM schemal.city;
D. SELECT * INTO schema2.eity FROM schemal.city;

Answer: B

NEW QUESTION # 33
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 arxl has a summarized Amount that is higher than 10,000. Which query should you use?

Answer: B

Explanation:
The correct query to use in order to return data for the year 2023 that displays ProductID, ProductName, and has a summarized Amount greater than 10,000 is Option B.
The reason is that it uses the GROUP BY clause to organize the data by ProductID and ProductName and then filters the result using the HAVING clause to only include groups where the sum of Amount is greater than 10,000. Additionally, the DATEPART(YEAR, SaleDate) = '2023' part of the HAVING clause ensures that only records from the year 2023 are included. Reference = For more information, please visit the official documentation on T-SQL queries and the GROUP BY clause at T-SQL GROUP BY.

NEW QUESTION # 34
Hotspot Question
You have a Microsoft Power Bl project that contains a file named definition.pbir. definition.pbir contains the following JSON.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
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 # 35
You need to resolve the issue with the pricing group classification.
How should you complete the T-SQL statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

NEW QUESTION # 36
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Superb DP-600 Exam Questions Supply You Marvelous Learning Dumps - DumpExam

Microsoft DP-600 Pass Exam | Related DP-600 Exams

Microsoft DP-600 Exam Syllabus Topics:

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

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