Question # 1
You have a Fabric tenant that contains a semantic model. The model contains 15 tables.
You need to programmatically change each column that ends in the word Key to meet the following requirements:
• Hide the column.
• Set Nullable to False.
• Set Summarize By to None
• Set Available in MDX to False.
• Mark the column as a key column.
What should you use?
|
| A. Microsoft Power Bl Desktop | | B. Tabular Editor | | C. ALM Toolkit | | D. DAX Studio |
B. Tabular Editor
Explanation:
Tabular Editor is an advanced tool for editing Tabular models outside of Power BI Desktop that allows you to script out changes and apply them across multiple columns or tables. To accomplish the task programmatically, you would:
Open the model in Tabular Editor.
Create an Advanced Script using C# to iterate over all tables and their respective columns.
Within the script, check if the column name ends with 'Key'.
For columns that meet the condition, set the properties accordingly: IsHidden = true, IsNullable = false, SummarizeBy = None, IsAvailableInMDX = false.
Additionally, mark the column as a key column.
Save the changes and deploy them back to the Fabric tenant.
References: The ability to batch-edit properties using scripts in Tabular Editor is well-documented in the tool's official documentation and user community resources.
Question # 2
You have a Fabric tenant that contains a semantic model.
You need to prevent report creators from populating visuals by using implicit measures.
What are two tools that you can use to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
|
| A. Microsoft Power BI Desktop | | B. Tabular Editor | | C. Microsoft SQL Server Management Studio (SSMS) | | D. DAX Studio |
A. Microsoft Power BI Desktop
B. Tabular Editor
Explanation:
Microsoft Power BI Desktop (A) and Tabular Editor (B) are the tools you can use to prevent report creators from using implicit measures. In Power BI Desktop, you can define explicit measures which can be used in visuals. Tabular Editor allows for advanced model editing, where you can enforce the use of explicit measures. References = Guidance on using explicit measures and preventing implicit measures in reports can be found in the Power BI and Tabular Editor official documentation.
Question # 3
| You have a Microsoft Power Bl semantic model that contains measures. The measures use multiple calculate functions and a filter function.
You are evaluating the performance of the measures.
In which use case will replacing the filter function with the keepfilters function reduce execution time? |
| A. when the filter function uses a nested calculate function | | B. when the filter function references a column from a single table that uses Import mode | | C. when the filter function references columns from multiple tables | | D. when the filter function references a measure |
A. when the filter function uses a nested calculate function
Explanation:
The KEEPFILTERS function modifies the way filters are applied in calculations done through the CALCULATE function. It can be particularly beneficial to replace the FILTER function with KEEPFILTERS when the filter context is being overridden by nested CALCULATE functions, which may remove filters that are being applied on a column. This can potentially reduce execution time because KEEPFILTERS maintains the existing filter context and allows the nested CALCULATE functions to be evaluated more efficiently.
References: This information is based on the DAX reference and performance optimization guidelines in the Microsoft Power BI documentation.
Question # 4
You have a Fabric tenant that contains a Microsoft Power Bl report named Report 1. Report1 includes a Python visual. Data displayed by the visual is grouped automatically and duplicate rows are NOT displayed. You need all rows to appear in the visual. What should you do?
|
| A. Reference the columns in the Python code by index. | | B. Modify the Sort Column By property for all columns. | | C. Add a unique field to each row. | | D. Modify the Summarize By property for all columns. |
A. Reference the columns in the Python code by index.
Explanation:
To ensure all rows appear in the Python visual within a Power BI report, option C, adding a unique field to each row, is the correct solution. This will prevent automatic grouping by unique values and allow for all instances of data to be represented in the visual.
References = For more on Power BI Python visuals and how they handle data, please refer to the Power BI documentation.
Question # 5
| You need to refresh the Orders table of the Online Sales department. The solution must meet the semantic model requirements. What should you include in the solution? |
| A. an Azure Data Factory pipeline that executes a dataflow to retrieve the minimum value of the OrderlD column in the destination lakehouse | | B. an Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the maximum value of the OrderlD column in the destination lakehouse | | C. an Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderlD column in the destination lakehouse | | D. an Azure Data Factory pipeline that executes a Stored procedure activity to retrieve the minimum value of the OrderiD column m the
destination lakehouse |
C. an Azure Data Factory pipeline that executes a dataflow to retrieve the maximum value of the OrderlD column in the destination lakehouse
Question # 6
| You need to ensure the data loading activities in the AnalyticsPOC workspace are executed in the appropriate sequence. The solution must meet the technical requirements.
What should you do? |
| A. Create a pipeline that has dependencies between activities and schedule the pipeline. | | B. Create and schedule a Spark job definition. | | C. Create a dataflow that has multiple steps and schedule the dataflow. | | D. Create and schedule a Spark notebook. |
A. Create a pipeline that has dependencies between activities and schedule the pipeline.
Explanation:
To meet the technical requirement that data loading activities must ensure the raw and cleansed data is updated completely before populating the dimensional model, you would need a mechanism that allows for ordered execution. A pipeline in Microsoft Fabric with dependencies set between activities can ensure that activities are executed in a specific sequence. Once set up, the pipeline can be scheduled to run at the required intervals (hourly or daily depending on the data source).
Question # 7
You have a Fabric tenant that contains a semantic model.
You need to prevent report creators from populating visuals by using implicit measures.
What are two tools that you can use to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct answer is worth one point.
|
| A. Microsoft Power BI Desktop | | B. Tabular Editor | | C. Microsoft SQL Server Management Studio (SSMS) | | D. DAX Studio |
A. Microsoft Power BI Desktop
B. Tabular Editor
Explanation:
Microsoft Power BI Desktop (A) and Tabular Editor (B) are the tools you can use to prevent report creators from using implicit measures. In Power BI Desktop, you can define explicit measures which can be used in visuals. Tabular Editor allows for advanced model editing, where you can enforce the use of explicit measures. References = Guidance on using explicit measures and preventing implicit measures in reports can be found in the Power BI and Tabular Editor official documentation.
Question # 8
You have a Fabric tenant that contains a semantic model. The model contains 15 tables.
You need to programmatically change each column that ends in the word Key to meet the following requirements:
• Hide the column.
• Set Nullable to False.
• Set Summarize By to None
• Set Available in MDX to False.
• Mark the column as a key column.
What should you use?
|
| A. Microsoft Power Bl Desktop | | B. Tabular Editor | | C. ALM Toolkit | | D. DAX Studio |
B. Tabular Editor
Explanation:
Tabular Editor is an advanced tool for editing Tabular models outside of Power BI Desktop that allows you to script out changes and apply them across multiple columns or tables. To accomplish the task programmatically, you would:
Open the model in Tabular Editor.
Create an Advanced Script using C# to iterate over all tables and their respective columns.
Within the script, check if the column name ends with 'Key'.
For columns that meet the condition, set the properties accordingly: IsHidden = true, IsNullable = false, SummarizeBy = None, IsAvailableInMDX = false.
Additionally, mark the column as a key column.
Save the changes and deploy them back to the Fabric tenant.
References: The ability to batch-edit properties using scripts in Tabular Editor is well-documented in the tool's official documentation and user community resources.
Question # 9
You need to implement the date dimension in the data store. The solution must meet the technical requirements.
What are two ways to achieve the goal? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
|
| A. Populate the date dimension table by using a dataflow. | | B. Populate the date dimension table by using a Stored procedure activity in a pipeline. | | C. Populate the date dimension view by using T-SQL. | | D. Populate the date dimension table by using a Copy activity in a pipeline. |
A. Populate the date dimension table by using a dataflow.
B. Populate the date dimension table by using a Stored procedure activity in a pipeline.
Explanation:
Both a dataflow (A) and a Stored procedure activity in a pipeline (B) are capable of creating and populating a date dimension table. A dataflow can perform the transformation needed to create the date dimension, and it aligns with the preference for using low-code tools for data ingestion when possible. A Stored procedure could be written to generate the necessary date dimension data and executed within a pipeline, which also adheres to the technical requirements for the PoC.
Question # 10
| You need to recommend which type of fabric capacity SKU meets the data analytics requirements for the Research division. What should you recommend? |
| A. EM | | B. F | | C. P | | D. A |
B. F
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Implementing Analytics Solutions Using Microsoft Fabric Exam Dumps
Exam Code: DP-600
Exam Name: Implementing Analytics Solutions Using Microsoft Fabric
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Questions People Ask About DP-600 Exam
DP-600 is considered challenging due to its focus on both the design and implementation of Microsoft's data platform solutions. Those with a strong data analytics background and some Azure experience may find it less challenging. Dedicated study is essential regardless.
Implementing Analytics Solutions Using Microsoft Fabric exam assess a candidate’s ability to deploy and manage analytics solutions using Microsoft Service Fabric.
Microsoft Service Fabric is a distributed systems platform used to build scalable, reliable, and easily managed applications for the cloud. It is designed to facilitate the development and management of microservices and container-based applications across a cloud environment.
Preparing for the DP-600 exam involves several steps:
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1. Familiarize yourself with the Microsoft Fabric Analytics Engineer Associate exam objectives and structure by reviewing the official Microsoft certification guide.
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2. Engage in hands-on practice with Azure data services, focusing on areas such as Azure SQL Database, Azure Synapse Analytics, and Azure Cosmos DB.
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3. Utilize Microsoft's learning paths and training materials specifically designed for this exam.
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4. Hands-On Azure: Experiment, especially with data services on Azure.
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5. Take advantage of online courses and tutorials from platforms like Pluralsight, Dumps4free, Udemy, or LinkedIn Learning.
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6. Practice with sample questions and DP-600 mock exams to gauge your readiness and identify areas needing improvement.
Azure: Broad cloud platform offering a huge range of services (compute, storage, databases, etc.)
Service Fabric: More specialized, used to build scalable, microservice-based applications that can run on Azure or even on-premises.
No, Databricks is not part of Microsoft Azure Service Fabric. Here's the distinction:
• Databricks: A unified analytics platform focused on data engineering, data science, and machine learning, often built on top of cloud services.
• Azure Service Fabric: A platform specifically for developing and deploying microservices-based applications.
Service Fabric is a platform for creating and managing microservices and container-based applications, specifically designed to enhance the development of complex, scalable applications within the Azure cloud environment. Therefore, it will not replace Microsoft Azure but instead continues to function as an integral tool within Azure, supporting its capabilities in cloud computing and application management.
Microsoft Azure Service Fabric is most similar to other microservices and container orchestration platforms like:
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• Kubernetes: Popular open-source system for managing containers, widely used in cloud environments.
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• Apache Mesos: Another orchestration platform, often used for large-scale, distributed applications.
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• Amazon ECS: AWS's container orchestration service focusing on simplicity.
Service Fabric is a platform for developing and managing scalable, distributed applications using microservices and containers, primarily on Azure. In contrast, Snowflake is a cloud-based data warehousing service that provides a powerful solution for data storage, processing, and analytics, without the need for infrastructure management.
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