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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
  • 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.

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q82-Q87):

NEW QUESTION # 82
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 # 83
You have a Fabric tenant that contains a warehouse.
You use a dataflow to load a new dataset from OneLake to the warehouse.
You need to add a Power Query step to identify the maximum values for the numeric columns.
Which function should you include in the step?

Answer: D


NEW QUESTION # 84
You have a Fabric tenant that contains a semantic model named Modell. Model! contains the following tables.

Product and Store can be part of multiple campaigns, and multiple active campaigns can exist simultaneously.
You plan to create a report named Report1 that will display sales made after a campaign for a given store.
You need to add a table to Modell and configure cardinality for the tables relationships. What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:


NEW QUESTION # 85
You have a Fabric workspace named Workspacel that contains a lakehouse named Lakehousel. Lakehousel contains a table named Tablel. Table 1 contains the following data.

You need to perform the following actions:
* Load the data from Table! into a star schema.
* Create a product dimension table named DimProduct and a fact table named FactSales.
Which three columns should you include in DimProduct?

Answer: A


NEW QUESTION # 86
You have a Microsoft Power BI project that contains a file named definition.pbir. definition.pbir contains the following JSON.

Answer:

Explanation:

Explanation:
PBIR-Legacy format # No
Located in Power BI service # No
Opens in full edit mode # Yes
Comprehensive Detailed Explanation
We are analyzing a Power BI Report (PBIR) definition file in JSON format:
{
" version " : " 1.0 " ,
" byPath " : {
" path " : " ../Sales.Dataset "
}
}
}
Step 1: Is this PBIR-Legacy?
PBIR-Legacy format is used when referencing a dataset hosted in the Power BI service (cloud).
In this case, the JSON uses byPath , which references a local dataset file ( Sales.Dataset ) in the project folder , not a service dataset.
Therefore: No , it is not PBIR-Legacy.
Step 2: Where is the semantic model located?
The datasetReference points to ../Sales.Dataset .
That means the semantic model is stored locally in the project structure , not in the Power BI service.
Therefore: No , it is not located in the Power BI service.
Step 3: What happens when opening in Power BI Desktop?
Since the dataset reference is local ( byPath ), Power BI Desktop opens the semantic model in full edit mode (allowing schema, relationships, measures, etc. to be modified).
If it were PBIR-Legacy with a service model, only Live Connection mode would be available.
Therefore: Yes , Power BI Desktop opens it in full edit mode.
References
PBIR file structure in Power BI projects
Semantic model references: byPath vs byConnection
PBIR-Legacy format # No
Located in Power BI service # No
Opens in full edit mode # Yes


NEW QUESTION # 87
......

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