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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Ingest and transform data | 30-35% | - Ingest and transform data using eventstreams - Ingest data using Data Factory pipelines and Dataflows Gen2 - Design and implement loading patterns - Optimize and manage Delta tables - Transform data using Dataflows Gen2 and notebooks |
| Topic 2: Monitor and optimize an analytics solution | 30-35% | - Optimize performance of data storage and queries - Monitor Fabric items - Implement error handling in pipelines |
| Topic 3: Implement and manage an analytics solution | 30-35% | - Orchestrate processes with Data Factory pipelines - Implement lifecycle management and source control for analytics assets - Implement security and governance - Configure and manage workspace settings and item access |
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NEW QUESTION # 136
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:
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 this is an SQL-like query and cannot be executed in KQL, which is required for the database.
Correct code should look like:
NEW QUESTION # 137
You have a Fabric workspace that contains two lakehouses named Lakehouse1 and Lakehouse2. Lakehouse1 contains staging data in a Delta table named Orderlines. Lakehouse2 contains a Type 2 slowly changing dimension (SCD) dimension table named Dim_Customer.
You need to build a query that will combine data from Orderlines and Dim_Customer to create a new fact table named Fact_Orders. The new table must meet the following requirements:
Enable the analysis of customer orders based on historical attributes.
Enable the analysis of customer orders based on the current attributes.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 138
You are building a Fabric notebook named MasterNotebookl in a workspace. MasterNotebookl contains the following code.
You need to ensure that the notebooks are executed in the following sequence:
1. Notebook_03
2. Notebook.Ol
3. Notebook_02
Which two actions should you perform? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.
Answer: E,F
NEW QUESTION # 139
You are building a data orchestration pattern by using a Fabric data pipeline named Dynamic Data Copy as shown in the exhibit. (Click the Exhibit tab.)
Dynamic Data Copy does NOT use parametrization.
You need to configure the ForEach activity to receive the list of tables to be copied.
How should you complete the pipeline expression? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 140
You have an Azure key vault named KeyVaultl that contains secrets.
You have a Fabric workspace named Workspace!. Workspace! contains a notebook named Notebookl that performs the following tasks:
* Loads stage data to the target tables in a lakehouse
* Triggers the refresh of a semantic model
You plan to add functionality to Notebookl that will use the Fabric API to monitor the semantic model refreshes. You need to retrieve the registered application ID and secret from KeyVaultl to generate the authentication token. Solution: You use the following code segment:
Use notebookutils. credentials.getSecret and specify key vault URL and the name of a linked service.
Does this meet the goal?
Answer: B
NEW QUESTION # 141
......
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