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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
  • 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 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 (Q38-Q43):

NEW QUESTION # 38
You have a Fabric tenant that contains two lakehouses.
You are building a dataflow that will combine data from the lakehouses. The applied steps from one of the queries in the dataflow is shown in the following exhibit.

Use the drop-down menus to select the answer choice that completes each statement based on the information presented in the graphic. NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Folding in Power Query refers to operations that can be translated into source queries. In this case, "some" of the steps can be folded, which means that some transformations will be executed at the data source level. The steps that cannot be folded will be executed within the Power Query engine. Custom steps, especially those that are not standard query operations, are usually executed within Power Query engine rather than being pushed down to the source system.
References =
Query folding in Power Query
Power Query M formula language


NEW QUESTION # 39
You have a Fabric tenant.
You are creating a Fabric Data Factory pipeline.
You have a stored procedure that returns the number of active customers and their average sales for the current month.
You need to add an activity that will execute the stored procedure in a warehouse. The returned values must be available to the downstream activities of the pipeline.
Which type of activity should you add?

Answer: B


NEW QUESTION # 40
You have a Fabric eventhouse that contains a KQL database. The database contains a table named TaxiData that stores the following data.

You need to create a column named FirstPickupDateTime that will contain the first value of each hour from tpep_pickup_datetime partitioned by payment_type.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:

Explanation:

Comprehensive Detailed Explanation
We have a KQL table (TaxiData) with columns:
VendorID
tpep_pickup_datetime (timestamp)
payment_type
total_amount
The requirement:
Create a new column FirstPickupDateTime
It should contain the first pickup timestamp per hour
Partitioning should be done by payment_type
Step 1: Which windowing function?
row_cumsum # running cumulative sum (not needed here).
row_rank_dense # assigns ranks without gaps, but does not guarantee minimum value only.
row_rank_min # gives the first/minimum value in each window partition. # Correct.
row_window_session # sessionization of events, not required.
So, the correct function is row_rank_min.
Step 2: Which comparison operator?
We need to select the row where the rank = 1 (the first per partition).
So the correct operator is == (equals).
Step 3: Partitioning
The KQL query should partition by:
bin(tpep_pickup_datetime, 1h) # buckets data into 1-hour windows
payment_type # partitions further by payment type
Completed KQL Query
TaxiData
| sort by tpep_pickup_datetime asc, payment_type asc
| extend FirstPickupDateTime = row_rank_min(tpep_pickup_datetime, 1h, 0m, payment_type)
| where FirstPickupDateTime == 1
This assigns a rank within each 1-hour, per-payment-type window, then keeps the first pickup timestamp.
Why This Works
row_rank_min # ensures we capture the first occurrence in each hour.
== # filters only the first row per partition.
bin(..., 1h) ensures grouping is by hour.
References
Kusto row_rank_min() function
KQL window functions


NEW QUESTION # 41
You have a Fabric tenant that contains two workspaces named Workspace1 and Workspace2.
Workspace1 is used as the development environment.
Workspace2 is used as the production environment.
Each environment uses a different storage account.
Workspace1 contains a Dataflow Gen2 named Dataflow1. The data source of Dataflow1 is a CSV file in blob storage.
You plan to implement a deployment pipeline to deploy items from Workspace1 to Workspace2.
You need to ensure that the data source references the correct location in the production environment.
What should you do?

Answer: B

Explanation:
Scenario:
Dev = Workspace1 with Dataflow Gen2 (source = blob storage CSV).
Prod = Workspace2, different storage account.
Need: ensure deployed dataflow points to the production storage location .
Analysis:
Data source rules : used to remap data sources between environments (e.g., blob storage dev # blob storage prod).
Parameter rules : used when the data source location is parameterized (for example, a parameter storing the file path or connection string).
Best practice: use parameters in the dataflow for connection strings, then apply parameter rules in deployment pipelines.
In this case, since the requirement is about ensuring the reference updates correctly, only parameter rules are needed (not data source rules).
.


NEW QUESTION # 42
You have a Fabric tenant that contains a complex semantic model. The model is based on a star schema and contains many tables, including a fact table named Sales.
You need to visualize a diagram of the model. The diagram must contain only the Sales table and related tables.
What should you use from Microsoft Power BI Desktop?

Answer: A

Explanation:
The Model view in Microsoft Power BI Desktop allows you to visualize the relationships between tables in a semantic model. It displays a diagram of the data model, where you can focus on specific tables, such as the Sales fact table and its related tables, by arranging or filtering the view. This is the ideal tool for analyzing the structure of a star schema and understanding table relationships.


NEW QUESTION # 43
......

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