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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.

Microsoft Implementing Analytics Solutions Using Microsoft Fabric Sample Questions (Q171-Q176):

NEW QUESTION # 171
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: C


NEW QUESTION # 172
When working with Power Query dataflows in Microsoft Power BI, optimizing performance and reusability is critical. An effective strategy is to separate data injection from data transformation.
By doing so, you can improve the efficiency of data processing and ensure that dataflows are easy to manage and reuse. In addition, other best practices can further improve performance.
Which of the following practices are recommended for improving performance and reusability in Power Query dataflows? (Select 3)

Answer: A,B,E

Explanation:
Option A: Correct because this practice of reducing the frequency of data source access enhances performance by minimizing the overhead associated with repeated data pulls. By isolating data ingestion processes, it also increases reusability, as data can be processed once and reused across multiple analyses or applications without the need for redundant extraction operations.
Option C: Correct because filtering data at the source before bringing it into the analytical environment reduces the volume of data that needs to be processed. This approach minimizes resource consumption and improves query performance by reducing the amount of data transferred across the network and processed by downstream systems.
Option E: Correct because query folding is a technique where transformations applied in a query are pushed back to the data source. This approach leverages the data source's processing capabilities to perform transformations, reducing the workload on the analytical dataflow or processing engine. It enhances performance by minimizing data movement and optimizing query execution efficiency.
Reference:
https://learn.microsoft.com/en-us/power-query/dataflows/performance-ingestion-transformation- dataflows


NEW QUESTION # 173
You have a Fabric tenant.
You plan to create a data pipeline named Pipeline1. Pipeline1 will include two activities that will execute in sequence.
You need to ensure that a failure of the first activity will NOT block the second activity.
Which conditional path should you configure between the first activity and the second activity?

Answer: B


NEW QUESTION # 174
You have a Fabric tenant.
You plan to create a Fabric notebook that will use Spark DataFrames to generate Microsoft Power Bl visuals.
You run 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 code embeds an existing Power BI report. - No
* The code creates a Power BI report. - Yes
* The code displays a summary of the DataFrame. - Yes
The code provided seems to be a snippet from a SQL query or script which is neither creating nor embedding a Power BI report directly. It appears to be setting up a DataFrame for use within a larger context, potentially for visualization in Power BI, but the code itself does not perform the creation or embedding of a report.
Instead, it's likely part of a data processing step that summarizes data.
References =
* Introduction to DataFrames - Spark SQL
* Power BI and Azure Databricks


NEW QUESTION # 175
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: A

Explanation:
Lookup activity reads and returns the content of a configuration file or table. It also returns the result of executing a query or stored procedure. The output can be a singleton value or an array of attributes, which can be consumed in a subsequent copy, transformation, or control flow activities like ForEach activity.
https://learn.microsoft.com/en-us/azure/data-factory/control-flow-lookup-activity


NEW QUESTION # 176
......

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