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NEW QUESTION # 43
You have a Fabric tenant that contains a semantic model named Modell.
Model1 contains a table named TemperatureUnit. TemperatureUnit contains a column named Unit. Unit contains manually added options foe Celsius and Fahrenheit.
Modell contains the following, measures:
A measure that contains the following expression.
A measure that contains the following expression:
[TemperatureMetric] = AVERAGE(Weather[TempC] )
A dynamic measure that contains the following expression:
[TemperatureDisplay] = VAR Unit = SELECTEDVALUE(TemperatureUnit[Unit] , " Celsius " ) VAR CelsiusValue = [TemperatureMetric] RETURN SWITCH( Unit, " Fahrenheit " , (CelsiusValue * 9/5) + 32, CelsiusValue ) You need to add a dynamic format string to the [TemperatureDisplay] measure to enable users to switch display values between metric (Celsius) and imperial (Fahrenheit).
How should you complete the DAX expression? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 44
You have a Fabric tenant that contains a workspace named Workspace1 and a user named User1. Workspace1 contains a warehouse named DW1.
You share DW1 with User1 and assign User1 the default permissions for DW1.
What can User1 do?
Answer: B
NEW QUESTION # 45
You have a Fabric eventhouse that contains a KQL database. The database contains a table named TaxiData that stores the following data.
You need to create a column named FirstPickupDateTime that will contain the first value of each hour from tpep_pickup_datetime partitioned by payment_type.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Comprehensive Detailed Explanation
We have a KQL table (TaxiData) with columns:
VendorID
tpep_pickup_datetime (timestamp)
payment_type
total_amount
The requirement:
Create a new column FirstPickupDateTime
It should contain the first pickup timestamp per hour
Partitioning should be done by payment_type
Step 1: Which windowing function?
row_cumsum # running cumulative sum (not needed here).
row_rank_dense # assigns ranks without gaps, but does not guarantee minimum value only.
row_rank_min # gives the first/minimum value in each window partition. # Correct.
row_window_session # sessionization of events, not required.
So, the correct function is row_rank_min.
Step 2: Which comparison operator?
We need to select the row where the rank = 1 (the first per partition).
So the correct operator is == (equals).
Step 3: Partitioning
The KQL query should partition by:
bin(tpep_pickup_datetime, 1h) # buckets data into 1-hour windows
payment_type # partitions further by payment type
Completed KQL Query
TaxiData
| sort by tpep_pickup_datetime asc, payment_type asc
| extend FirstPickupDateTime = row_rank_min(tpep_pickup_datetime, 1h, 0m, payment_type)
| where FirstPickupDateTime == 1
This assigns a rank within each 1-hour, per-payment-type window, then keeps the first pickup timestamp.
Why This Works
row_rank_min # ensures we capture the first occurrence in each hour.
== # filters only the first row per partition.
bin(..., 1h) ensures grouping is by hour.
References
Kusto row_rank_min() function
KQL window functions
NEW QUESTION # 46
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?
Answer: A
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. Reference = For more on Power BI Python visuals and how they handle data, please refer to the Power BI documentation.
Topic 2, Contoso, ltd.
Overview
Contoso, ltd. is a US-based health supplements company, Contoso has two divisions named Sales and Research. The Sales division contains two departments named Online Sales and Retail Sales. The Research division assigns internally developed product lines to individual teams of researchers and analysts.
Identity Environment
Contoso has a Microsoft Entra tenant named contoso.com. The tenant contains two groups named ResearchReviewersGroupi and ReseachReviewefsGfoup2.
Data Environment
Contoso has the following data environment
* The Sales division uses a Microsoft Power B1 Premium capacity.
* The semantic model of the Online Sales department includes a fact table named Orders that uses import mode. In the system of origin, the OrderlD value represents the sequence in which orders are created.
* The Research department uses an on-premises. third-party data warehousing product.
* Fabric is enabled for contoso.com.
* An Azure Data Lake Storage Gen2 storage account named storage1 contains Research division data for a product line named Producthne1. The data is in the delta format.
* A Data Lake Storage Gen2 storage account named storage2 contains Research division data for a product line named Productline2. The data is in the CSV format.
Planned Changes
Contoso plans to make the following changes:
* Enable support for Fabric in the Power Bl Premium capacity used by the Sales division.
* Make all the data for the Sales division and the Research division available in Fabric.
* For the Research division, create two Fabric workspaces named Producttmelws and Productline2ws.
* in Productlinelws. create a lakehouse named LakehouseV
* In Lakehouse1. create a shortcut to storage1 named ResearchProduct.
Data Analytics Requirements
Contoso identifies the following data analytics requirements:
* All the workspaces for the Sales division and the Research division must support all Fabric experiences.
* The Research division workspaces must use a dedicated, on-demand capacity that has per-minute billing.
* The Research division workspaces must be grouped together logically to support OneLake data hub filtering based on the department name.
* For the Research division workspaces, the members of ResearchRevtewersGroupl must be able to read lakehouse and warehouse data and shortcuts by using SQL endpoints.
* For the Research division workspaces, the members of ResearchReviewersGroup2 must be able to read lakehouse data by using Lakehouse explorer.
* All the semantic models and reports for the Research division must use version control that supports branching Data Preparation Requirements Contoso identifies the following data preparation requirements:
* The Research division data for Producthne2 must be retrieved from Lakehouset by using Fabric notebooks.
* All the Research division data in the lakehouses must be presented as managed tables in Lakehouse explorer.
Semantic Model Requirements
Contoso identifies the following requirements for implementing and managing semantic models;
* The number of rows added to the Orders table during refreshes must be minimized.
* The semantic models in the Research division workspaces must use Direct Lake mode.
General Requirements
Contoso identifies the following high-level requirements that must be considered for all solutions:
* Follow the principle of least privilege when applicable
* Minimize implementation and maintenance effort when possible.
NEW QUESTION # 47
You have a Fabric warehouse named Warehousel that contains a table named Table! Tablel contains customer data.
You need to implement row-level security (RLS) for Tablel. The solution must ensure that users can see only their respective data.
Which two objects should you create? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
Answer: A,C
Explanation:
To implement row-level security (RLS) in a Fabric Warehouse (like in Azure Synapse or SQL Server):
You must define a predicate function (usually an inline table-valued function) that filters rows for each user # Function.
Then you bind that function to the table using a Security Policy # Security Policy.
A Database Role is used for group-based access control but not specifically for implementing RLS.
Stored procedures and constraints are not used for RLS.
Correct answers: A and D
Reference: Row-Level Security (RLS) in Synapse/Fabric SQL
NEW QUESTION # 48
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