Explanation:
A replica of dbo.Sales is created in the test schema by copying the metadata only. - Yes Additional schema changes to dbo.FactSales will also apply to test.FactSales. - No Additional data changes to dbo.FactSales will also apply to test.FactSales. - No The CREATE TABLE AS CLONE statement creates a copy of an e xisting table, including its data and any associated data structures, like indexes. Therefore, the statement does not merely copy metadata; it also copies the data. However, subsequent schema changes to the original table do not automatically propagate to the cloned table. Any data changes in the original table after the clone operation will not be reflected in the clone unless explicitly updated.
References =
CREATE TABLE AS SELECT (CTAS) in SQL Data Warehouse

Explanation:

We are designing a star schema for sales transactions. In a star schema:
Dimension tables contain descriptive attributes (e.g., Customer, Product, Date).
Fact tables contain measurable, quantitative data (e.g., SalesTransactionID, Quantity, UnitPrice).
Factless fact tables capture events without numeric measures.
Junk dimensions store miscellaneous low-cardinality attributes.
The requirement says dimension tables will use Type 2 Slowly Changing Dimensions (SCD) # so Customer and Date attributes belong to Dimension tables .
Field-by-field classification
CustomerCode
A business key (natural key) for the customer dimension.
Belongs in the Customer Dimension table.
answer: Dimension .
CustomerName
A descriptive attribute of the customer.
Belongs in the Customer Dimension table.
answer: Dimension .
SaleDate
A time attribute used for analysis.
Belongs in the Date Dimension table.
answer: Dimension .
Final answer:
CustomerCode # Dimension
CustomerName # Dimension
SaleDate # Dimension
References:
Star schema design for Power BI / Fabric
Slowly Changing Dimensions in data warehousing
# This follows Kimball's star schema best practices.

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

Explanation:

Comprehensive Detailed Explanation
We need to query a KQL database table named Readings to generate output that includes both the current reading and the previous reading values and datetime for each row.
Step 1: Understanding Requirements
The output table shows:
City, Area, MeterReading, Datetime
PrevMeterReading, PrevDatetime
The prev() function in KQL is used to access the value of a column from the previous row (based on order).
Step 2: Query Construction
We start with filtering:
Readings
| where City == " Copenhagen "
| sort by Datetime
To add columns that calculate the previous reading and previous datetime , we use extend :
| extend PrevMeterReading = prev(MeterReading), PrevDatetime = prev(Datetime) extend creates new columns.
prev() pulls the previous row's values.
Finally, we only want specific columns in the output. For this, we use project :
| project City, Area, MeterReading, Datetime, PrevMeterReading, PrevDatetime Step 3: Completed Query Readings
| where City == " Copenhagen "
| sort by Datetime
| extend PrevMeterReading = prev(MeterReading), PrevDatetime = prev(Datetime)
| project City, Area, MeterReading, Datetime, PrevMeterReading, PrevDatetime Why This is Correct extend adds calculated columns.
prev() gives access to the previous row.
project selects only the required columns.
This matches exactly the table shown in the question.
References
Kusto Query Language - extend operator
Kusto Query Language - project operator
prev() function in KQL

Explanation:
Comprehensive Detailed Explanation
When assigning permissions to roles in a Fabric workspace for analytics workloads, you must align permissions with the responsibilities and tools typically used by each persona:
1. Data Engineers
Their primary tasks include building and transforming data pipelines, as well as making datasets usable by others.
The appropriate permission is Build Reports on the default dataset , because this allows them to create and manage reports using the curated semantic model while preparing data for analysts and scientists.
2. Data Analysts
Analysts work heavily with Apache Spark for exploratory analysis, cleansing, and shaping data.
The correct permission is Read All Apache Spark , as this grants them the ability to query and analyze Spark- based data directly without elevated permissions that are unnecessary for their role.
3. Data Scientists
Data scientists often use SQL analytics endpoints to run queries, train models, and integrate data into machine learning workflows.
The correct permission is Read All SQL analytics endpoint data , since it enables direct programmatic access to the SQL endpoint, which is needed for advanced modeling and experimentation.
Summary of Se lections:
DataEngineers # Build Reports on the default dataset
DataAnalysts # Read All Apache Spark
DataScientists # Read All SQL analytics endpoint data
References (Microsoft Fabric - DP-600 exam scope):
Workspace roles and permissions in Microsoft Fabric
SQL analytics endpoint in Fabric
Apache Spark in Microsoft Fabric