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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 (Q54-Q59):

NEW QUESTION # 54
You have a Fabric workspace that uses the default Spark starter pool and runtime version 1,2.
You plan to read a CSV file named Sales.raw.csv in a lakehouse, select columns, and save the data as a Delta table to the managed area of the lakehouse. Sales_raw.csv contains 12 columns.
You have 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:

Explanation:

* The Spark engine will read only the 'SalesOrderNumber', 'OrderDate', 'CustomerName', 'UnitPrice' columns from Sales_raw.csv. - Yes
* Removing the partition will reduce the execution time of the query. - No
* Adding inferSchema='true' to the options will increase the execution time of the query. - Yes The code specifies the selection of certain columns, which means only those columns will be read into the DataFrame. Partitions in Spark are a way to optimize the execution of queries by organizing the data into parts that can be processed in parallel. Removing the partition could potentially increase the execution time because Spark would no longer be able to process the data in parallel efficiently. The inferSchema option allows Spark to automatically detect the column data types, which can increase the execution time of the initial read operation because it requires Spark to read through the data to infer the schema.


NEW QUESTION # 55
Hotspot Question
You have a Fabric tenant that contains a warehouse named Warehouse1. Warehouse1 contains a fact table named FactSales that has one billion rows.
You run the following T-SQL statement.
CREATE TABLE test.FactSales AS CLONE OF Dbo.FactSales;
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:
The AS CLONE AS creates a replica of the original table by copying the metadata (no data).
The two copies are independent therefore any changes will not be inherited.
https://learn.microsoft.com/en-us/fabric/data-warehouse/clone-table


NEW QUESTION # 56
You need to recommend a solution to prepare the tenant for the PoC.
Which two actions should you recommend performing from the Fabric Admin portal? Each correct answer presents part of the solution.
NOTE: Each correct answer is worth one point.

Answer: A,E

Explanation:
The PoC is planned to be completed using a Fabric trial capacity, which implies that users involved in the PoC should be able to try paid features. However, this should be limited to specific security groups involved in the PoC to prevent the entire organization from accessing these features before the trial is proven successful (A).
The ability for users to create Fabric items should also be enabled for specific security groups to ensure that only the relevant team members participating in the PoC can create items in the Fabric environment (E).


NEW QUESTION # 57
Drag and Drop Question
You have a Fabric tenant that contains a Microsoft Power BI report named Report1.
Report1 is slow to render. You suspect that an inefficient DAX query is being executed.
You need to identify the slowest DAX query, and then review how long the query spends in the formula engine as compared to the storage engine.
Which five actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

Answer:

Explanation:


NEW QUESTION # 58
You have the following KQL query.

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:

We are analyzing this KQL query:
Sales
| where Status != " Cancelled "
| where OrderDate > = ago(30d)
| summarize TotalSales = sum(SalesAmount) by ProductCategory
| where TotalSales > 0
Statement 1:
" The query excludes sales that have a Status of Cancelled. "
Yes # where Status != " Cancelled " filters them out.
Statement 2:
" The query calculates the total sales of each product category for the last 30 days. " Yes # where OrderDate > = ago(30d) ensures only last 30 days.
summarize TotalSales = sum(SalesAmount) by ProductCategory groups by category.
Statement 3:
" The query includes product categories that have had zero sales during the last 30 days. " No # The final filter where TotalSales > 0 excludes categories with zero sales.
Final Answer:
Excludes Cancelled sales # Yes
Calculates total sales by product category for last 30 days # Yes
Includes product categories with zero sales # No
References:
KQL where operator
KQL summarize operator


NEW QUESTION # 59
......

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