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| Section | Weight | Objectives |
|---|---|---|
| Monitor and optimize an analytics solution | 30-35% | - Monitor Fabric items - Implement error handling in pipelines - Optimize performance of data storage and queries |
| Ingest and transform data | 30-35% | - Design and implement loading patterns - Optimize and manage Delta tables - Ingest data using Data Factory pipelines and Dataflows Gen2 - Transform data using Dataflows Gen2 and notebooks - Ingest and transform data using eventstreams |
| Implement and manage an analytics solution | 30-35% | - Implement security and governance - Implement lifecycle management and source control for analytics assets - Orchestrate processes with Data Factory pipelines - Configure and manage workspace settings and item access |
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NEW QUESTION # 18
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: B
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 # 19
You have a Google Cloud Storage (GCS) container named storage1 that contains the files shown in the following table.
You have a Fabric workspace named Workspace1 that has the cache for shortcuts enabled. Workspace1 contains a lakehouse named Lakehouse1. Lakehouse1 has the shortcuts shown in the following table.
You need to read data from all the shortcuts.
Which shortcuts will retrieve data from the cache?
Answer: C
Explanation:
When reading data from shortcuts in Fabric (in this case, from a lakehouse like Lakehouse1), the cache for shortcuts helps by storing the data locally for quick access. The last accessed timestamp and the cache expiration rules determine whether data is fetched from the cache or from the source (Google Cloud Storage, in this case).
Products: The ProductFile.parquet was last accessed 12 hours ago. Since the cache has data available for up to
12 hours, it is likely that this data will be retrieved from the cache, as it hasn ' t been too long since it was last accessed.
Stores: The StoreFile.json was last accessed 4 hours ago, which is within the cache retention period.
Therefore, this data will also be retrieved from the cache.
Trips: The TripsFile.csv was last accessed 48 hours ago. Given that it ' s outside the typical caching window (assuming the cache has a maximum retention period of around 24 hours), it would not be retrieved from the cache. Instead, it will likely require a fresh read from the source.
NEW QUESTION # 20
You have an Azure Event Hubs data source that contains weather data.
You ingest the data from the data source by using an eventstream named Eventstream1. Eventstream1 uses a lakehouse as the destination.
You need to batch ingest only rows from the data source where the City attribute has a value of Kansas. The filter must be added before the destination. The solution must minimize development effort.
What should you use for the data processor and filtering? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 21
You have a Fabric workspace that contains an eventhouse named Eventhouse1.
In Eventhouse1, you plan to create a table named DeviceStreamData in a KQL database. The table will contain data based on the following sample.
Answer:
Explanation:
Explanation:
NEW QUESTION # 22
You have a Fabric workspace named Workspace1 that contains three notebooks named notebook_01, notebook_02, and notebook_03.
You are building a new notebook in Workspace1 that will contain the following Directed Acyclic Graph (DAG) definition.
You need to modify the DAG definition to meet the following requirements:
* Ensure that notebook.01 and notebook_02 run in parallel.
* Ensure that notebook_02 only runs after the execution of notebook_03 is complete.
How should you modify the DAG definition?
Answer: A
NEW QUESTION # 23
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