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Microsoft DP-600 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 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.

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q158-Q163):

NEW QUESTION # 158
You have two Microsoft Power Bl queries named Employee and Retired Roles.
You need to merge the Employee query with the Retired Roles query. The solution must ensure that rows in the Employee query that match the Retired Roles query are removed.
Which column and Join Kind should you use in Power Query Editor? To answer, select the appropriate options in the answer area.
NOTE: Each correct answer is worth one point

Answer:

Explanation:

Explanation:


NEW QUESTION # 159
You have a semantic model named Model1 that contains data that relates to customers and their bank account balances.
Model1 has the following tables and columns.

A customer can have one or more accounts. Each account can be associated to multiple customers.
You need to ensure that users can query Model1 to identify the total transaction amounts by customer.
What should you add to Model1?

Answer: B

Explanation:
Scenario:
Tables:
DimAccount (accounts)
FactTransaction (transactions)
DimCustomer (customers)
Business rule:
A customer can have multiple accounts .
An account can be linked to multiple customers .
Many-to-many relationship.
Requirement: Calculate total transaction amounts by customer .
Analysis:
Direct many-to-many between FactTransaction and DimCustomer would not properly resolve aggregation.
Adding CustomerKey directly in FactTransaction is not possible because transactions link to accounts, not customers directly.
Correct approach: Create a bridge table (CustomerAccount) that maps Customer # Account .
Then link FactTransaction # DimAccount # Bridge # DimCustomer .


NEW QUESTION # 160
You need to create a DAX measure to calculate the average overall satisfaction score.
How should you complete the DAX code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
* The measure should use the AVERAGE function to calculate the average value.
* It should reference the Response Value column from the 'Survey' table.
* The 'Number of months' should be used to define the period for the average calculation.
To calculate the average overall satisfaction score using DAX, you would need to use the AVERAGE function on the response values related to satisfaction questions. The DATESINPERIOD function will help in calculating the rolling average over the last 12 months.


NEW QUESTION # 161
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 # 162
Hotspot Question
You have a data warehouse that contains the following tables:
- DimCustomer
- DimEmployee
- DimGeography
- FactInternetSales
Each table has a primary key and the following relationships:
- CustomerKey = FactInternetSales.CustomerKey
- EmployeeKey = FactInternetSales.EmployeeKey
- Geography = DimCustomer.GeographyKey
You have a T-SQL query named Query1 that contains the following statements.

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 # 163
......

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