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Microsoft DP-600 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 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
  • 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 (Q140-Q145):

NEW QUESTION # 140
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric tenant that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Customer.
When you query Customer, you discover that the query is slow to execute. You suspect that maintenance was NOT performed on the table.
You need to identify whether maintenance tasks were performed on Customer.
Solution: You run the following Spark SQL statement: DESCRIBE HISTORY customer Does this meet the goal?

Answer: A


NEW QUESTION # 141
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 # 142
Hotspot Question
You have a Fabric tenant that contains a lakehouse.
You are using a Fabric notebook to save a large DataFrame by using the following code:
df.write.partitionBy("year", "month",
"day").mode("overwrite").parquet("Files/SalesOrder")
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 # 143
You have a Fabric warehouse that contains a table named Staging.Sales. Staging.Sales contains the following columns.

You need to write a T-SQL query that will return data for the year 2023 that displays ProductID and ProductName arxl has a summarized Amount that is higher than 10,000. Which query should you use?

Answer: B


NEW QUESTION # 144
You have source data in a folder on a local computer.
You need to create a solution that will use Fabric to populate a data store. The solution must meet the following requirements:
* Support the use of dataflows to load and append data to the data store.
* Ensure that Delta tables are V-Order optimized and compacted automatically.
Which type of data store should you use?

Answer: D


NEW QUESTION # 145
......

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