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Microsoft DP-600 Exam Syllabus Topics:

TopicDetails
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
  • 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.
Topic 3
  • 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.

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q16-Q21):

NEW QUESTION # 16
You have a Fabric tenant that contains a semantic model.
You need to prevent report creators from populating visuals by using implicit measures.
What are two tools that you can use to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.

Answer: B,C

Explanation:
Microsoft Power BI Desktop (A) and Tabular Editor (B) are the tools you can use to prevent report creators from using implicit measures. In Power BI Desktop, you can define explicit measures which can be used in visuals. Tabular Editor allows for advanced model editing, where you can enforce the use of explicit measures. References = Guidance on using explicit measures and preventing implicit measures in reports can be found in the Power BI and Tabular Editor official documentation.


NEW QUESTION # 17
You have a Fabric workspace that contains a warehouse named DW1. DW1 contains the following tables and columns.

You need to summarize order quantities by year and product. The solution must include the yearly sum of order quantities for all the products in each row.
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:

Explanation:

Comprehensive Detailed Explanation
We need to write a query that summarizes order quantities by year and product , and also include the yearly total for all products in each row.
Step 1: What the query needs
Extract the year from SalesOrderDetail.ModifiedDate .
Join with the Product table to get the product name.
Aggregate with SUM(OrderQty) .
Return grouped data by year and product , with an additional subtotal row per year (all products combined).
Step 2: Evaluate the SELECT Clause
We must extract the year using:
YEAR (so.ModifiedDate) AS OrderDate
This converts the ModifiedDate column into a year value for grouping.
Step 3: Evaluate the GROUP BY options
CUBE(YEAR, P.Name) # Produces all combinations of year totals, product totals, and grand totals. Too many combinations, not required.
GROUPING SETS # Could achieve the result but requires explicitly listing the sets. Less direct.
ROLLUP(YEAR, P.Name) # Produces grouping by (Year, Product) and then a subtotal per Year. Exactly what is required.
YEAR only # Would group only by year, losing per-product breakdown.
Correct: ROLLUP(YEAR(so.ModifiedDate), P.Name)
Step 4: Completed Query
SELECT
YEAR (so.ModifiedDate) AS OrderDate,
p.Name,
SUM (so.OrderQty) AS OrderQty
FROM dbo.SalesOrderDetail so
INNER JOIN dbo.Product p
ON p.ProductID = so.ProductID
GROUP BY ROLLUP ( YEAR (so.ModifiedDate), p.Name);
Why This Works
YEAR(so.ModifiedDate) extracts year for grouping.
ROLLUP(YEAR, P.Name) provides both product-level totals and yearly subtotals.
Ensures the requirement: "include the yearly sum of order quantities for all the products in each row." References GROUP BY ROLLUP in T-SQL Aggregate functions in Microsoft Fabric warehouses


NEW QUESTION # 18
You need to refresh the Orders table of the Online Sales department. The solution must meet the semantic model requirements. What should you include in the solution?

Answer: C

Explanation:
destination lakehouse


NEW QUESTION # 19
You have a KQL database that contains a table named Readings.
You need to query Readings and return the results shown in the following table.

How should you complete the query? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Comprehensive Detailed Explanation
We need to query a KQL database table named Readings to generate output that includes both the current reading and the previous reading values and datetime for each row.
Step 1: Understanding Requirements
The output table shows:
City, Area, MeterReading, Datetime
PrevMeterReading, PrevDatetime
The prev() function in KQL is used to access the value of a column from the previous row (based on order).
Step 2: Query Construction
We start with filtering:
Readings
| where City == " Copenhagen "
| sort by Datetime
To add columns that calculate the previous reading and previous datetime , we use extend :
| extend PrevMeterReading = prev(MeterReading), PrevDatetime = prev(Datetime) extend creates new columns.
prev() pulls the previous row's values.
Finally, we only want specific columns in the output. For this, we use project :
| project City, Area, MeterReading, Datetime, PrevMeterReading, PrevDatetime Step 3: Completed Query Readings
| where City == " Copenhagen "
| sort by Datetime
| extend PrevMeterReading = prev(MeterReading), PrevDatetime = prev(Datetime)
| project City, Area, MeterReading, Datetime, PrevMeterReading, PrevDatetime Why This is Correct extend adds calculated columns.
prev() gives access to the previous row.
project selects only the required columns.
This matches exactly the table shown in the question.
References
Kusto Query Language - extend operator
Kusto Query Language - project operator
prev() function in KQL


NEW QUESTION # 20
You have a Fabric workspace named Workspace1 and an Azure Data Lake Storage Gen2 account named storage"!. Workspace1 contains a lakehouse named Lakehouse1.
You need to create a shortcut to storage! in Lakehouse1.
Which connection and endpoint should you specify? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

When creating a shortcut to an Azure Data Lake Storage Gen2 account in a lakehouse, you should use the abfss (Azure Blob File System Secure) connection string and the dfs (Data Lake File System) endpoint. The abfss is used for secure access to Azure Data Lake Storage, and the dfs endpoint indicates that the Data Lake Storage Gen2 capabilities are to be used.


NEW QUESTION # 21
......

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