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| Section | Weight | Objectives |
|---|---|---|
| Clean, transform, and enrich data | 25-30% | - Transform data
|
| Load and prepare data | 20-25% | - Create and configure items
|
| Design and manage the data model | 20-25% | - Implement and configure a data model
|
| Secure and monitor data solutions | 15-20% | - Monitor data solutions
|
| Deploy and maintain a data solution | 10-15% | - Deploy data assets
|
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NEW QUESTION # 81
You have a Fabric tenant that contains a lakehouse.
You are using a Fabric notebook to save a large DataFrame by using the following code.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
The results will form a hierarchy of folders for each partition key. - Yes The resulting file partitions can be read in parallel across multiple nodes. - Yes The resulting file partitions will use file compression. - No Partitioning data by columns such as year, month, and day, as shown in the DataFrame write operation, organizes the output into a directory hierarchy that reflects the partitioning structure. This organization can improve the performance of read operations, as queries that filter by the partitioned columns can scan only the relevant directories. Moreover, partitioning facilitates parallelism because each partition can be processed independently across different nodes in a distributed system like Spark. However, the code snippet provided does not explicitly specify that file compression should be used, so we cannot assume that the output will be compressed without additional context.
References =
DataFrame write partitionBy
Apache Spark optimization with partitioning
NEW QUESTION # 82
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 # 83
You are creating a semantic model in Microsoft Power BI Desktop.
You plan to make bulk changes to the model by using the Tabular Model Definition Language (TMDL) extension for Microsoft Visual Studio Code.
You need to save the semantic model to a file.
Which file format should you use?
Answer: C
Explanation:
The PBIP will create one file and two folders, PBIP.Dataset contains definition folder that is use to host the .tmdl files.
NEW QUESTION # 84
You have a Fabric tenant that contains a data warehouse.
You need to load rows into a large Type 2 slowly changing dimension (SCD). The solution must minimize resource usage. Which T-SQL statement should you use?
Answer: B
Explanation:
For Type 2 Slowly Changing Dimensions (SCD):
MERGE is the standard T-SQL approach, allowing conditional UPDATE for existing rows and INSERT for new history records, in a single statement.
UPDATE + INSERT would require multiple steps and higher resource usage.
TRUNCATE + INSERT would destroy historical data, violating SCD rules.
CREATE TABLE AS SELECT is not suitable for incremental Type 2 handling.
Correct answer: B.
Reference: Implement SCD Type 2 with MERGE
NEW QUESTION # 85
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 # 86
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