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Microsoft DP-700 考試大綱：

主題	簡介
主題 1	Ingest and transform data: This section of the exam measures the skills of Data Engineers that cover designing and implementing data loading patterns. It emphasizes preparing data for loading into dimensional models, handling batch and streaming data ingestion, and transforming data using various methods. A skill to be measured is applying appropriate transformation techniques to ensure data quality.
主題 2	Monitor and optimize an analytics solution: This section of the exam measures the skills of Data Analysts in monitoring various components of analytics solutions in Microsoft Fabric. It focuses on tracking data ingestion, transformation processes, and semantic model refreshes while configuring alerts for error resolution. One skill to be measured is identifying performance bottlenecks in analytics workflows.
主題 3	Implement and manage an analytics solution: This section of the exam measures the skills of Microsoft Data Analysts regarding configuring various workspace settings in Microsoft Fabric. It focuses on setting up Microsoft Fabric workspaces, including Spark and domain workspace configurations, as well as implementing lifecycle management and version control. One skill to be measured is creating deployment pipelines for analytics solutions.

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最新的 Microsoft Certified: Fabric Data Engineer Associate DP-700 免費考試真題 (Q91-Q96):

問題 #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?

A. No
B. Yes

答案：A

解題說明：
An outer join will include unmatched rows from both tables, increasing the dataset size and processing time. It does not improve query performance.

問題 #92
You have a Fabric workspace that contains a write intensive warehouse named DW1. DW1 stores staging tables that are used to load a dimensional model. The tables are often read once, dropped, and then recreated to process new data.
You need to minimize load time of data from sources to staging tables in DW1.
What should you do?

A. Disable V Order.
B. Drop statistics.
C. Create statistics.
D. Enable V Order.

答案：A

問題 #93
You have a Fabric workspace that contains a Real-Time Intelligence solution and an eventhouse.
Users report that from OneLake file explorer, they cannot see the data from the eventhouse.
You enable OneLake availability for the eventhouse.
What will be copied to OneLake?

A. only new data added to the eventhouse
B. no data
C. both new data and existing data in the eventhouse
D. only the existing data in the eventhouse
E. only data added to new databases that are added to the eventhouse

答案：A

解題說明：
When you enable OneLake availability for an eventhouse, both new and existing data in the eventhouse will be copied to OneLake. This feature ensures that data, whether newly ingested or already present, becomes available for access through OneLake, making it easier for users to interact with and explore the data directly from OneLake file explorer.

問題 #94
You have a Fabric workspace named Workspace! that contains an Azure Data Factory pipeline named Pipeline1 You need to monitor the execution and activity status of Pipeline1 and automatically email an alert if the pipeline fails. The solution must minimize development and administrative effort.
What should you use to monitor Pipeline1, and what should you use to email the alert? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

答案：

解題說明：

Explanation:

問題 #95
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?

A. Products, Stores, and Trips
B. Stores and Products only
C. Trips only
D. Stores only
E. Products only
F. Products and Trips only

答案：B

解題說明：
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.

問題 #96
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