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Microsoft DP-600 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 (Q176-Q181):

NEW QUESTION # 176
You have source data in a folder on a local computer.
You need to create a solution that will use Fabric to populate a data store. The solution must meet the following requirements:
* Support the use of dataflows to load and append data to the data store.
* Ensure that Delta tables are V-Order optimized and compacted automatically.
Which type of data store should you use?

Answer: B

Explanation:
A lakehouse (A) is the type of data store you should use. It supports dataflows to load and append data and ensures that Delta tables are Z-Order optimized and compacted automatically. References = The capabilities of a lakehouse and its support for Delta tables are described in the lakehouse and Delta table documentation.


NEW QUESTION # 177
You need to create a data loading pattern for a Type 1 slowly changing dimension (SCD).
Which two actions should you include in the process? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.

Answer: A,C

Explanation:
For a Type 1 SCD, you should include actions that update rows when non-key attributes have changed (A), and insert new records when the natural key is a new value in the table (D). A Type 1 SCD does not track historical data, so you always overwrite the old data with the new data for a given key. Reference = Details on Type 1 slowly changing dimension patterns can be found in data warehousing literature and Microsoft's official documentation.


NEW QUESTION # 178
You are creating a dataflow in Fabric to ingest data from an Azure SQL database by using a T-SQL statement.
You need to ensure that any foldable Power Query transformation steps are processed by the Microsoft SQL Server engine.
How should you complete the code? To answer, drag the appropriate values to the correct targets. Each value may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:
You should complete the code as follows:
* Table
* NativeQuery
* EnableFolding
In Power Query, using Table before the SQL statement ensures that the result of the SQL query is treated as a table. NativeQuery allows a native database query to be passed through from Power Query to the source database. The EnableFolding option ensures that any subsequent transformations that can be folded will be sent back and executed at the source database (Microsoft SQL Server engine in this case).


NEW QUESTION # 179
You have a Fabric warehouse that contains two tables named DimDate and Trips.
DimDate contains the following fields.

Trips contains the following fields.

You need to compare the average miles per trip for statutory holidays versus non-statutory holidays.
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
Step 1: Requirement
We need to compare the average miles per trip for:
Statutory holidays (when IsHoliday = 1 )
Non-statutory holidays (when IsHoliday = 0 )
Step 2: Formula for average miles per trip
Average miles per trip = total miles ÷ number of trips
Total miles # SUM(t.tripDistance)
Number of trips # COUNT(t.tripID)
So the calculation is:
( SUM (t.tripDistance) / COUNT (t.tripID)) AS MilesPerTrip
Step 3: Grouping
We need a comparison by holiday status .
So we must group the results by:
GROUP BY d.IsHoliday
This ensures we get two rows: one for IsHoliday = 1 and one for IsHoliday = 0 .
Step 4: Final Query
SELECT
d.IsHoliday,
( SUM (t.tripDistance) / COUNT (t.tripID)) AS MilesPerTrip
FROM DimDate d
INNER JOIN Trips t ON d.DateID = t.DateID
GROUP BY d.IsHoliday;
Why This is Correct
The formula ensures average miles per trip .
Grouping ensures comparison between holidays vs non-holidays .
Efficient aggregation, minimal computation.
References
Aggregate functions in T-SQL
GROUP BY clause


NEW QUESTION # 180
You have a Fabric tenant that contains a warehouse.
A user discovers that a report that usually takes two minutes to render has been running for 45 minutes and has still not rendered.
You need to identify what is preventing the report query from completing.
Which dynamic management view (DMV) should you use?

Answer: C

Explanation:
The correct DMV to identify what is preventing the report query from completing is sys.
dm_pdw_exec_requests (D). This DMV is specific to Microsoft Analytics Platform System (previously known as SQL Data Warehouse), which is the environment assumed to be used here. It provides information about all queries and load commands currently running or that have recently run. References = You can find more about DMVs in the Microsoft documentation for Analytics Platform System.


NEW QUESTION # 181
......

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