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Microsoft DP-600 Prüfungsplan:

ThemaEinzelheiten
Thema 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.
Thema 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.
Thema 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 DP-600 Prüfungsfragen mit Lösungen (Q191-Q196):

191. Frage
You have a Fabric workspace named Workspace 1 that contains a dataflow named Dataflow1. Dataflow! has a query that returns 2.000 rows. You view the query in Power Query as shown in the following exhibit.

What can you identify about the pickupLongitude column?

Antwort: D


192. Frage
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?

Antwort: D


193. Frage
You have the source data model shown in the following exhibit.

The primary keys of the tables are indicated by a key symbol beside the columns involved in each key.
You need to create a dimensional data model that will enable the analysis of order items by date, product, and customer.
What should you include in the solution? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:

* The relationship between OrderItem and Product must be based on: Both the CompanyID and the ProductID columns
* The Company entity must be: Denormalized into the Customer and Product entities In a dimensional model, the relationships are typically based on foreign key constraints between the fact table (OrderItem) and dimension tables (Product, Customer, Date). Since CompanyID is present in both the OrderItem and Product tables, it acts as a foreign key in the relationship. Similarly, ProductID is a foreign key that relates these two tables. To enable analysis by date, product, and customer, the Company entity would need to be denormalized into the Customer and Product entities to ensure that the relevant company information is available within those dimensions for querying and reporting purposes.
References =
* Dimensional modeling
* Star schema design


194. Frage
You have a Fabric tenant that contains a warehouse.
You are designing a star schema model that will contain a customer dimension. The customer dimension table will be a Type 2 slowly changing dimension (SCD).
You need to recommend which columns to add to the table. The columns must NOT already exist in the source.
Which three types of columns should you recommend? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.

Antwort: A,C,E

Begründung:
To create SCD type 2 one needs to add a surrogate key + start/end date beside the other technical attributes.
https://learn.microsoft.com/en-us/training/modules/populate-slowly-changing-dimensions-azure- synapse-analytics-pipelines/3-choose-between-dimension-types


195. Frage
You have a Fabric tenant that contains a semantic model named Modell. Model! 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 Modell and configure cardinality for the tables relationships. What should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:


196. Frage
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