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Microsoft DP-600 Exam Overview:

Certification Vendor:Microsoft
Exam Name:Implementing Analytics Solutions Using Microsoft Fabric
Exam Number:DP-600
Available Languages:English, Chinese (Simplified), Japanese, Korean
Certificate Validity Period:1 year
Real Exam Qty:40-60
Related Certifications:Microsoft Certified: Fabric Analytics Engineer Associate
Exam Duration:120 minutes
Passing Score:700/1000
Exam Format:Multiple-choice, Drag-and-drop, Case studies
Exam Price:$165 USD
Sample Questions:Microsoft DP-600 Sample Questions
Exam Way:Online proctored or in-person testing center
Pre Condition:Candidates should have foundational knowledge of data concepts, experience with Microsoft Fabric, and proficiency in data transformation and modeling. Familiarity with Power BI is recommended but not required.
Official Syllabus URL:https://learn.microsoft.com/en-us/certifications/exams/dp-600

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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.

Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q200-Q205):

NEW QUESTION # 200
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 # 201
You have a Fabric tenant that contains a lakehouse named Lakehouse1.
You need to prevent new tables added to Lakehouse1 from being added automatically to the default semantic model of the lakehouse.
What should you configure?

Answer: A

Explanation:
You need to prevent new tables added to Lakehouse1 from being added automatically to the default semantic model of the lakehouse.
https://learn.microsoft.com/en-us/fabric/data-warehouse/semantic-models but "Sync the default Power BI semantic model".


NEW QUESTION # 202
You have an Azure subscription that contains two users named User1 and User2.
You have a Fabric tenant that has certification and master data endorsement enabled. User2 ts configured as an authorized receiver for certification and master data endorsement. The tenant contains a workspace named Workspace V Workspace1 contains the following items:
* A semantic model named Model1
o User1 is the owner of Modell and is assigned write access to the model
* A lakehouse named LH1 mat stores the customer master data

Answer:

Explanation:

Explanation:


NEW QUESTION # 203
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 # 204
You need to design a semantic model for the customer satisfaction report.
Which data source authentication method and mode should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 205
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