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| Section | Objectives |
|---|
| Topic 1: Design and implement data ingestion solutions | - Integrate external data sources
- 1. Connect to on-premises and SaaS sources
- 2. Connect to Azure data services
- Ingest batch and streaming data into Microsoft Fabric
- 1. Use Pipelines for data movement
- 2. Use Dataflows Gen2 for ingestion
- 3. Ingest streaming data using Real-Time Analytics
|
| Topic 2: Manage and monitor data solutions | - Monitor pipelines and workloads
- 1. Monitor data pipeline runs
- 2. Configure alerts and logging
- Optimize performance and reliability
- 1. Spark job optimization
- 2. Capacity and workload management in Fabric
|
| Topic 3: Develop data transformation solutions | - Data modeling and lakehouse transformation
- 1. Medallion architecture (Bronze/Silver/Gold)
- 2. Delta Lake optimization
- Transform data using Spark in Fabric
- 1. Notebooks in Microsoft Fabric
- 2. PySpark data processing
|
| Topic 4: Secure and govern data in Microsoft Fabric | - Implement data security
- 1. Row-level security and column-level security
- 2. Workspace and item-level permissions
- Data governance and compliance
- 1. Microsoft Purview integration
- 2. Data lineage and cataloging
|
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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q89-Q94):
NEW QUESTION # 89
You have a Fabric workspace that contains a warehouse named Warehouse!. Warehousel contains a table named DimCustomers. DimCustomers contains the following columns:
* CustomerName
* CustomerlD
* BirthDate
* Email
You need to configure security to meet the following requirements:
* BirthDate in DimCustomer must be masked and display 1900-01-01.
* Email in DimCustomer must be masked and display only the first leading character and the last five characters.
How should you complete the statement? To answer, select the appropriate options in the answer are a. NOTE: Each correct selection is worth one point.

Answer:
Explanation:

NEW QUESTION # 90
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a Fabric eventstream that loads data into a table named Bike_Location in a KQL database. The table contains the following columns:
BikepointID
Street
Neighbourhood
No_Bikes
No_Empty_Docks
Timestamp
You need to apply transformation and filter logic to prepare the data for consumption. The solution must return data for a neighbourhood named Sands End when No_Bikes is at least 15. The results must be ordered by No_Bikes in ascending order.
Solution: You use the following code segment:

Does this meet the goal?
Answer: B
Explanation:
This code does not meet the goal because it uses order by, which is not valid in KQL. The correct term in KQL is sort by.
Correct code should look like:

NEW QUESTION # 91
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a KQL database that contains two tables named Stream and Reference. Stream contains streaming data in the following format.

Reference contains reference data in the following format.

Both tables contain millions of rows.
You have the following KQL queryset.

You need to reduce how long it takes to run the KQL queryset.
Solution: You change the join type to kind=outer.
Does this meet the goal?
Answer: B
Explanation:
An outer join will include unmatched rows from both tables, increasing the dataset size and processing time. It does not improve query performance.
NEW QUESTION # 92
You have a Fabric workspace that contains a data pipeline named Pipeline! as shown in the exhibit.
(Click the Exhibit tab.) What will occur the next time Pipelinel tuns?

- A. Execute procedure1 will run first, and then Copy_kdi will run.
- B. Both activities will be skipped.
- C. Execute procedurel will run and Copy_kdi will be skipped.
- D. Copy.kdi will run first, and then Execute procedurel will run.
- E. Copy.kdi will run and Execute procedurel will be skipped.
- F. Both activities will run simultaneously.
Answer: B
NEW QUESTION # 93
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You have a KQL database that contains two tables named Stream and Reference. Stream contains streaming data in the following format.

Reference contains reference data in the following format.

Both tables contain millions of rows.
You have the following KQL queryset.

You need to reduce how long it takes to run the KQL queryset.
Solution: You move the filter to line 02.
Does this meet the goal?
Answer: A
Explanation:
Moving the filter to line 02: Filtering the Stream table before performing the join operation reduces the number of rows that need to be processed during the join. This is an effective optimization technique for queries involving large datasets.
NEW QUESTION # 94
......
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