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| Section | Weight | Objectives |
|---|---|---|
| Deploy and maintain a data solution | 10-15% | - Deploy data assets
|
| Load and prepare data | 20-25% | - Ingest data from source systems
|
| Clean, transform, and enrich data | 25-30% | - Clean data
|
| Secure and monitor data solutions | 15-20% | - Secure data solutions
|
| Design and manage the data model | 20-25% | - Implement and configure a data model
|
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NEW QUESTION # 107
You have a Microsoft Power Bl report named Report1 that uses a Fabric semantic model.
Users discover that Report1 renders slowly.
You open Performance analyzer and identify that a visual named Orders By Date is the slowest to render. The duration breakdown for Orders By Date is shown in the following table.
What will provide the greatest reduction in the rendering duration of Report1?
Answer: C
Explanation:
Based on the duration breakdown provided, the major contributor to the rendering duration is categorized as
"Other," which is significantly higher than DAX Query and Visual display times. This suggests that the issue is less likely with the DAX calculation or visual rendering times and more likely related to model performance or the complexity of the visual. However, of the options provided, optimizing the DAX query can be a crucial step, even if "Other" factors are dominant. Using DAX Studio, you can analyze and optimize the DAX queries that power your visuals for performance improvements. Here's how you might proceed:
* Open DAX Studio and connect it to your Power BI report.
* Capture the DAX query generated by the Orders By Date visual.
* Use the Performance Analyzer feature within DAX Studio to analyze the query.
* Look for inefficiencies or long-running operations.
* Optimize the DAX query by simplifying measures, removing unnecessary calculations, or improving iterator functions.
* Test the optimized query to ensure it reduces the overall duration.
References: The use of DAX Studio for query optimization is a common best practice for improving Power BI report performance as outlined in the Power BI documentation.
NEW QUESTION # 108
Hotspot Question
You have a Fabric warehouse that contains a table named Table1. Table1 contains the following data.
You need to create a T-SQL statement that meets the following requirements:
- Outputs the item name of each item and returns a null value if the item name is longer than 20 characters
- Outputs the PurchaseDate value in the format of mm/dd, yy
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 109
You have a Fabric workspace named Workspace1 that contains a lakehouse named Lakehouse1.
In Workspace1. you create a data pipeline named Pipeline1.
You have CSV files stored in an Azure Storage account.
You need to add an activity to Pipeline1 that will copy data from the CSV files to Lakehouse1. The activity must support Power Query M formula language expressions.
Which type of activity should you add?
Answer: D
Explanation:
The requirement is to copy data from CSV files in Azure Storage into a Fabric Lakehouse, and it must support Power Query M formula language.
Dataflow activity in Fabric pipelines allows you to use Power Query Online, which is based on the M formula language.
Notebook uses Spark (Python, PySpark, Scala, SQL), not M.
Copy data activity moves data but does not support Power Query M transformations.
Script is for T-SQL or stored procedure execution, not M.
Therefore, the correct activity is Dataflow.
Reference: Dataflows in Microsoft Fabric
NEW QUESTION # 110
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: C,D
Explanation:
A lakehouse (A) is the type of data store you should use. It supports dataflows to load and append data and ensures that Delta tables are Z-Order optimized and compacted automatically. References = The capabilities of a lakehouse and its support for Delta tables are described in the lakehouse and Delta table documentation.
NEW QUESTION # 111
You have a Microsoft Power BI semantic model that contains a table named Date. Date contains the following data.
You need to ensure that the visuals that use the Date table appear in chronological order. The solutions must minimize administrative effort.
What should you do?
Answer: B
Explanation:
To ensure that the visuals appear in chronological order (January through December) while minimizing administrative effort, you should perform the following action:
Sort the Fiscal Month column by the Month Number column.
Step-by-Step Instructions:
1. In Power BI Desktop, go to the Data view or Report view.
2. In the Data pane, select the Fiscal Month column (the text field).
3. On the ribbon, go to the Column tools tab.
4. Select Sort by column and choose Month Number from the dropdown list.
Why this is the correct approach:
Chronological Order: By default, Power BI sorts text columns (like "Jan", "Feb") alphabetically.
Sorting Fiscal Month by Month Number forces the visual to follow the numeric sequence (1, 2,
3...) instead of the alphabetical sequence (April, August, December...).
Minimized Effort: This is a "set it and forget it" configuration at the model level. Once this sort order is defined, every visual (Slicers, Bar Charts, Tables) created using the Fiscal Month column will automatically display in the correct chronological order without requiring manual sorting in every individual visual.
Reference:
https://community.fabric.microsoft.com/t5/Desktop/Sorting-by-Fiscal-Month/m-p/33299
NEW QUESTION # 112
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