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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 (Q128-Q133):

NEW QUESTION # 128
You have a data warehouse that contains the following tables:

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:

Explanation:


NEW QUESTION # 129
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 # 130
You have a Fabric tenant that contains a Microsoft Power Bl report named Report 1. Report1 includes a Python visual. Data displayed by the visual is grouped automatically and duplicate rows are NOT displayed. You need all rows to appear in the visual. What should you do?

Answer: C


NEW QUESTION # 131
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 # 132
You are analyzing customer purchases in a Fabric notebook by using PySpanc You have the following DataFrames:

You need to join the DataFrames on the customer_id column. The solution must minimize data shuffling. You write the following code.

Which code should you run to populate the results DataFrame?

Answer: C

Explanation:
The correct code to populate the results DataFrame with minimal data shuffling is Option A. Using the broadcast function in PySpark is a way to minimize data movement by broadcasting the smaller DataFrame ( customers) to each node in the cluster. This is ideal when one DataFrame is much smaller than the other, as in this case with customers. References = You can refer to the official Apache Spark documentation for more details on joins and the broadcast hint.


NEW QUESTION # 133
......

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