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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 (Q187-Q192):

NEW QUESTION # 187
Hotspot Question
You have a Fabric tenant that contains a PySpark notebook named Notebook1.
You define sas_token as a variable in the first cell of Notebook1 and store a shared access signature (SAS) token in the variable.
In the second cell, you run the following code.

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:


NEW QUESTION # 188
You need to create a Microsoft Power BI file that will be used to create multiple reports. The solution must meet the following requirements:
- The file must include predefined data source connections.
- The file must include the report structure and formatting.
- The file must NOT contain any data.
Which file format should you use?

Answer: A


NEW QUESTION # 189
Which type of data store should you recommend in the AnalyticsPOC workspace?

Answer: A


NEW QUESTION # 190
You have a Fabric notebook that has the Python code and output shown in the following exhibit.


Which type of analytics are you performing?

Answer: D

Explanation:
The Python code and output shown in the exhibit display a histogram, which is a representation of the distribution of data. This kind of analysis is descriptive analytics, which is used to describe or summarize the features of a dataset. Descriptive analytics answers the question of "what has happened" by providing insight into past data through tools such as mean, median, mode, standard deviation, and graphical representations like histograms.
References: Descriptive analytics and the use of histograms as a way to visualize data distribution are basic concepts in data analysis, often covered in introductory analytics and Python programming resources.


NEW QUESTION # 191
Hotspot Question
You have a Fabric tenant that contains a semantic model named Model1.
Model1 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 Model1 and configure cardinality for the table's 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:


NEW QUESTION # 192
......

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