Microsoft DP-700日本語版参考書 & DP-700過去問

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Microsoft DP-700 認定試験の出題範囲：

トピック出題範囲
トピック 1
  • 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.
トピック 2
  • 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.
トピック 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.

>> Microsoft DP-700日本語版参考書 <<

DP-700日本語版参考書 & 最新 DP-700過去問 「合格保証」を確実にする

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric 認定 DP-700 試験問題 (Q118-Q123):

質問 # 118
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.

正解：

解説：


質問 # 119
You are implementing the following data entities in a Fabric environment:
Entity1: Available in a lakehouse and contains data that will be used as a core organization entity Entity2: Available in a semantic model and contains data that meets organizational standards Entity3: Available in a Microsoft Power BI report and contains data that is ready for sharing and reuse Entity4: Available in a Power BI dashboard and contains approved data for executive-level decision making Your company requires that specific governance processes be implemented for the data.
You need to apply endorsement badges to the entities based on each entity's use case.
Which badge should you apply to each entity? To answer, drag the appropriate badges the correct entities.
Each badge may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation:


質問 # 120
You need to recommend a method to populate the POS1 data to the lakehouse medallion layers.
What should you recommend for each layer? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation:
A screenshot of a computer Description automatically generated

Bronze Layer: A pipeline Copy activity
The bronze layer is used to store raw, unprocessed data. The requirements specify that no transformations should be applied before landing the data in this layer. Using a pipeline Copy activity ensures minimal development effort, built-in connectors, and the ability to ingest the data directly into the Delta format in the bronze layer.
Silver Layer: A notebook
The silver layer involves extensive data cleansing (deduplication, handling missing values, and standardizing capitalization). A notebook provides the flexibility to implement complex transformations and is well-suited for this task.


質問 # 121
You have a Fabric warehouse named DW1 that contains four staging tables named ProductCategory, ProductSubcategory, Product, and SalesOrder. ProductCategory, ProductSubcategory, and Product are used often in analytical queries.
You need to implement a star schema for DW1. The solution must minimize development effort.
Which design approach should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

正解：

解説：

Explanation:


質問 # 122
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?

正解：A

解説：
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:


質問 # 123
......

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DP-700過去問: https://www.pass4test.jp/DP-700.html

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