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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
  • 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
  • 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.

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Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q92-Q97):

NEW QUESTION # 92
You have a Fabric tenant that contains a semantic model named model1. The two largest columns in model1 are shown in the following table.

You need to optimize model 1. The solution must meet the following requirements:
* Reduce the model size.
* Increase refresh performance when using Import mode.
* Ensure that the datetime value for each sales transaction is available in the model.
What should you do on each column? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

We are optimizing model1 in Fabric with two very large columns:
TransactionKey
Cardinality: 2.5 billion
Size: 160 GB
Description: surrogate key for SalesTransaction fact table.
SaleDateTime
Cardinality: 6.8 million
Size: 120 GB
Description: datetime (to the second) of when a sale occurred.
Goal:
Reduce model size.
Improve refresh performance (Import mode).
Ensure datetime values remain available.
Analysis per column
1. TransactionKey (very high cardinality)
Surrogate keys with extremely high cardinality do not add analytical value in a semantic model because they are not used in measures or grouping.
They take a lot of memory and reduce refresh performance.
Best practice: remove the column from the model.
2. SaleDateTime
Needed to analyze transactions by date and time.
However, storing the full datetime at second-level cardinality causes huge memory usage.
To optimize: split the column into Date and Time columns.
Reduces cardinality significantly.
Preserves full datetime information (can recombine if needed).
Improves compression and refresh speed.
Final Answer:
TransactionKey: Remove the column.
SaleDateTime: Split the column.
References:
Microsoft Fabric optimization guidance - reduce cardinality
Best practices for datetime optimization in semantic models
# Answer Selection:
TransactionKey # Remove the column
SaleDateTime # Split the column


NEW QUESTION # 93
You have a Fabric tenant that contains lakehouse named Lakehousel. Lakehousel contains a Delta table with eight columns. You receive new data that contains the same eight columns and two additional columns.
You create a Spark DataFrame and assign the DataFrame to a variable named df. The DataFrame contains the new data. You need to add the new data to the Delta table to meet the following requirements:
* Keep all the existing rows.
* Ensure that all the new data is added to the table.
How should you complete the code? To answer, select the appropriate options in the answer area.

Answer:

Explanation:

Explanation:

o add new data to the Delta table while meeting the specified requirements:
* You should use the append mode to ensure that all new data is added to the table without affecting the existing rows.
* You should set the mergeSchema option to true to allow the schema of the Delta table to be updated with the new columns found in the DataFrame.
The completed code would look like this:
df.write.format("delta").mode("append")
option("mergeSchema", "true")
saveAsTable("Lakehouse1.TableName")


NEW QUESTION # 94
Which workspace rote assignments should you recommend for ResearchReviewersGroupl and ResearchReviewersGroupZ? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

In Azure DevOps workspace (project) security settings, role assignments determine access levels for groups:
Viewer (equivalent to the Readers security group): Grants read-only access to work items, code, pipelines, and other artifacts. Ideal for review-only scenarios where users need to view but not edit content.
Contributor (equivalent to the Contributors security group): Allows full edit and contribute privileges, such as updating work items, code reviews, and pipeline runs. Suitable for active participation in reviews that may involve feedback or changes.
Based on the group names implying review-focused access, Viewer fits Group1 for passive observation, while Contributor fits Group2 for interactive review tasks. These align with default Azure DevOps permissions for secure, least-privilege access.


NEW QUESTION # 95
You are implementing two dimension tables named Customers and Products in a Fabric warehouse.
You need to use slowly changing dimension (SCO) to manage the versioning of data. The solution must meet the requirements shown in the following table.

Which type of SCD should you use for each table? To answer, drag the appropriate SCD types to the correct tables. Each SCD type 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:

For the Customers table, where the requirement is to create a new version of the row, you would use:
* Type 2 SCD: This type allows for the creation of a new record each time a change occurs, preserving the history of changes over time.
For the Products table, where the requirement is to overwrite the existing value in the latest row, you would use:
* Type 1 SCD: This type updates the record directly, without preserving historical data.


NEW QUESTION # 96
You have a Fabric workspace that contains a DirectQuery semantic model. The model queries a data source that has 500 million rows.
You have a Microsoft Power Bl report named Report1 that uses the model. Report! contains visuals on multiple pages.
You need to reduce the query execution time for the visuals on all the pages.
What are two features that you can use? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.

Answer: B,C

Explanation:
User-defined aggregations (A) and query caching (C) are two features that can help reduce query execution time. User-defined aggregations allow precalculation of large datasets, and query caching stores the results of queries temporarily to speed up future queries. References = Microsoft Power BI documentation on performance optimization offers in-depth knowledge on these features.


NEW QUESTION # 97
......

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