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Microsoft DP-700 Prüfungsplan:

ThemaEinzelheiten
Thema 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.
Thema 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.
Thema 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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DP-700 Pruefungssimulationen - DP-700 Prüfungen

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric DP-700 Prüfungsfragen mit Lösungen (Q53-Q58):

53. Frage
You need to create the product dimension.
How should you complete the Apache Spark SQL code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Explanation:
A screenshot of a computer Description automatically generated

Join between Products and ProductSubCategories:
Use an INNER JOIN.
The goal is to include only products that are assigned to a subcategory. An INNER JOIN ensures that only matching records (i.e., products with a valid subcategory) are included.
Join between ProductSubCategories and ProductCategories:
Use an INNER JOIN.
Similar to the above logic, we want to include only subcategories assigned to a valid product category. An INNER JOIN ensures this condition is met.
WHERE Clause
Condition: IsActive = 1
Only active products (where IsActive equals 1) should be included in the gold layer. This filters out inactive products.


54. Frage
You have a Fabric workspace that contains a lakehouse named Lakehouse1. Data is ingested into Lakehouse1 as one flat table. The table contains the following columns.

You plan to load the data into a dimensional model and implement a star schema. From the original flat table, you create two tables named FactSales and DimProduct. You will track changes in DimProduct.
You need to prepare the data.
Which three columns should you include in the DimProduct table? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

Antwort: C,E,F

Begründung:
In a star schema, the DimProduct table serves as a dimension table that contains descriptive attributes about products. It will provide context for the FactSales table, which contains transactional data. The following columns should be included in the DimProduct table:
* ProductName: The ProductName is an important descriptive attribute of the product, which is needed for analysis and reporting in a dimensional model.
* ProductColor: ProductColor is another descriptive attribute of the product. In a star schema, it makes sense to include attributes like color in the dimension table to help categorize products in the analysis.
* ProductID: ProductID is the primary key for the DimProduct table, which will be used to join the FactSales table to the product dimension. It's essential for uniquely identifying each product in the model.


55. Frage
You need to create the product dimension.
How should you complete the Apache Spark SQL code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Antwort:

Begründung:

Topic 2, Litware, Inc
Overview
This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.
To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.
Overview
Litware, Inc. is a publishing company that has an online bookstore and several retail bookstores worldwide. Litware also manages an online advertising business for the authors it represents.
Existing Environment. Fabric Environment
Litware has a Fabric workspace named Workspace1. High concurrency is enabled for Workspace1.
The company has a data engineering team that uses Python for data processing.
Existing Environment. Data Processing
The retail bookstores send sales data at the end of each business day, while the online bookstore constantly provides logs and sales data to a central enterprise resource planning (ERP) system.
Litware implements a medallion architecture by using the following three layers: bronze, silver, and gold. The sales data is ingested from the ERP system as Parquet files that land in the Files folder in a lakehouse. Notebooks are used to transform the files in a Delta table for the bronze and silver layers. The gold layer is in a warehouse that has V-Order disabled.
Litware has image files of book covers in Azure Blob Storage. The files are loaded into the Files folder.
Existing Environment. Sales Data
Month-end sales data is processed on the first calendar day of each month. Data that is older than one month never changes.
In the source system, the sales data refreshes every six hours starting at midnight each day.
The sales data is captured in a Dataflow Gen1 dataflow. When the dataflow runs, new and historical data is captured. The dataflow captures the following fields of the source:
Sales Date
Author
Price
Units
SKU
A table named AuthorSales stores the sales data that relates to each author. The table contains a column named AuthorEmail. Authors authenticate to a guest Fabric tenant by using their email address.
Existing Environment. Security Groups
Litware has the following security groups:
Sales
Fabric Admins
Streaming Admins
Existing Environment. Performance Issues
Business users perform ad-hoc queries against the warehouse. The business users indicate that reports against the warehouse sometimes run for two hours and fail to load as expected. Upon further investigation, the data engineering team receives the following error message when the reports fail to load: "The SQL query failed while running." The data engineering team wants to debug the issue and find queries that cause more than one failure.
When the authors have new book releases, there is often an increase in sales activity. This increase slows the data ingestion process.
The company's sales team reports that during the last month, the sales data has NOT been up-to-date when they arrive at work in the morning.
Requirements. Planned Changes
Litware recently signed a contract to receive book reviews. The provider of the reviews exposes the data in Amazon Simple Storage Service (Amazon S3) buckets.
Litware plans to manage Search Engine Optimization (SEO) for the authors. The SEO data will be streamed from a REST API.
Requirements. Version Control
Litware plans to implement a version control solution in Fabric that will use GitHub integration and follow the principle of least privilege.
Requirements. Governance Requirements
To control data platform costs, the data platform must use only Fabric services and items. Additional Azure resources must NOT be provisioned.
Requirements. Data Requirements
Litware identifies the following data requirements:
Process the SEO data in near-real-time (NRT).
Make the book reviews available in the lakehouse without making a copy of the data.
When a new book cover image arrives in the Files folder, process the image as soon as possible.


56. Frage
You have a Fabric data warehouse that contains the following tables.

You need to refresh the tables by using an automated pipeline. The solution must ensure that table updates occur in the correct order to maintain referential integrity.
Which two tables should you refresh first? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

Antwort: B,C


57. Frage
You have a Fabric workspace that contains a warehouse named DW1. DW1 is loaded by using a notebook named Notebook1.
You need to identify which version of Delta was used when Notebook1 was executed.
What should you use?

Antwort: D


58. Frage
......

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