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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Deploy and maintain a data solution | 10-15% | - Maintain a data solution
|
| Topic 2: Secure and monitor data solutions | 15-20% | - Monitor data solutions
|
| Topic 3: Design and manage the data model | 20-25% | - Design a data model
|
| Topic 4: Load and prepare data | 20-25% | - Ingest data from source systems
|
| Topic 5: Clean, transform, and enrich data | 25-30% | - Clean data
|
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NEW QUESTION # 72
You have a Fabric tenant that contains a lakehouse named LH1.
You create new tables in LH1.
You need to ensure that the tables are added automatically to the default semantic model.
What should you do?
Answer: D
Explanation:
To add objects such as tables or views to the default Power BI semantic model, you have options:
1. Manually enable the Sync the default Power BI semantic model setting that will automatically add objects to the semantic model. For more information, see Sync the default Power BI semantic model.
2. Manually add objects to the semantic model.
https://learn.microsoft.com/en-us/fabric/data-warehouse/default-power-bi-semantic-mode
NEW QUESTION # 73
You have a Fabric workspace that contains a large warehouse.
You plan to create a lakehouse named Lakehousel for a sales dataset. Lakehousel will contain the following tables:
* Sales: Contains sales transactions
* Stores: Contains a unique list of store names and locations
* Loyalty: Contains a list of customers and their preferred stores
* Customers: Contains a unique list of customer names and addresses
* Products: Contains a unique list of available products and their descriptions You need to configure a star schema for Lakehousel.
Which table should you define as the fact table, and which type of relationship should you configure from the Sales table to the Customers table? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 74
What should you recommend using to ingest the customer data into the data store in the AnatyticsPOC workspace?
Answer: A
Explanation:
For ingesting customer data into the data store in the AnalyticsPOC workspace, a dataflow (D) should be recommended. Dataflows are designed within the Power BI service to ingest, cleanse, transform, and load data into the Power BI environment. They allow for the low-code ingestion and transformation of data as needed by Litware's technical requirements. References = You can learn more about dataflows and their use in Power BI environments in Microsoft's Power BI documentation.
NEW QUESTION # 75
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: B
NEW QUESTION # 76
You have a Fabric tenant that contains a semantic model named model1. The two largest columns in model1 are shown in the following table.
You need to optimize model 1. The solution must meet the following requirements:
* Reduce the model size.
* Increase refresh performance when using Import mode.
* Ensure that the datetime value for each sales transaction is available in the model.
What should you do on each column? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
We are optimizing model1 in Fabric with two very large columns:
TransactionKey
Cardinality: 2.5 billion
Size: 160 GB
Description: surrogate key for SalesTransaction fact table.
SaleDateTime
Cardinality: 6.8 million
Size: 120 GB
Description: datetime (to the second) of when a sale occurred.
Goal:
Reduce model size.
Improve refresh performance (Import mode).
Ensure datetime values remain available.
Analysis per column
1. TransactionKey (very high cardinality)
Surrogate keys with extremely high cardinality do not add analytical value in a semantic model because they are not used in measures or grouping.
They take a lot of memory and reduce refresh performance.
Best practice: remove the column from the model.
2. SaleDateTime
Needed to analyze transactions by date and time.
However, storing the full datetime at second-level cardinality causes huge memory usage.
To optimize: split the column into Date and Time columns.
Reduces cardinality significantly.
Preserves full datetime information (can recombine if needed).
Improves compression and refresh speed.
Final Answer:
TransactionKey: Remove the column.
SaleDateTime: Split the column.
References:
Microsoft Fabric optimization guidance - reduce cardinality
Best practices for datetime optimization in semantic models
# Answer Selection:
TransactionKey # Remove the column
SaleDateTime # Split the column
NEW QUESTION # 77
......
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