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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Implementing Data Engineering Solutions Using Microsoft Fabric |
| Exam Number: | DP-700 |
| Passing Score: | 700 / 1000 |
| Certificate Validity Period: | 12 Months (Renewable annually) |
| Exam Price: | $165 USD |
| Exam Duration: | 100 minutes |
| Related Certifications: | Microsoft Certified: Fabric Data Engineer Associate |
| Available Languages: | English, French, Portuguese (Brazil), Simplified Chinese, German, Spanish, Japanese |
| Real Exam Qty: | 50 - 60 |
| Exam Format: | Drag-and-drop, Scenario-based, Case studies, Multiple choice |
| Sample Questions: | Microsoft DP-700 Sample Questions |
| Exam Way: | Online proctored or Test center |
| Pre Condition: | No specific prior certification required. |
| Official Syllabus URL: | https://learn.microsoft.com/en-us/credentials/certifications/exams/dp-700/ |
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NEW QUESTION # 108
You have a Fabric eventstream that ingests events from an Azure Event Hubs source named input1. You compute an aggregate by using a 60-second tumbling window.
You need to ensure that the window meets the following requirements:
* Provides updated results every five seconds.
* Ensures that each update reflects a rolling, 60-second history of events.
What should you do?
Answer: C
Explanation:
A hopping window is the correct window type when an aggregation must cover a fixed historical duration while producing results at a shorter recurring interval. In this case, the required historical range is 60 seconds , while a new calculation must be produced every five seconds . Therefore, configuring a hopping window with a 60-second window size and five-second hop creates overlapping 60-second windows whose start points are five seconds apart. Each output consequently reflects the most recent 60-second period while being recalculated every five seconds.
A tumbling window behaves differently. Tumbling windows divide the event stream into fixed, non- overlapping intervals . A 60-second tumbling window therefore does not provide a rolling result every five seconds. Reducing it to five seconds would increase output frequency but would calculate only five seconds of history.
Microsoft ' s Eventstream SQL documentation demonstrates HoppingWindow specifically for rolling-window scenarios, such as evaluating a five-minute interval every minute. Microsoft Learn Filtering timestamps does not change the aggregation window semantics, and increasing the tumbling duration to 65 seconds does not increase output frequency.
Study Guide reference: Ingest and transform data # Real-Time Intelligence # Eventstreams # windowed aggregation # tumbling and hopping windows.
NEW QUESTION # 109
You are processing streaming data from an external data provider.
You have the following code segment.
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Topic 2, Contoso, Ltd
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. Company Overview
Contoso, Ltd. is an online retail company that wants to modernize its analytics platform by moving to Fabric. The company plans to begin using Fabric for marketing analytics.
Overview. IT Structure
The company's IT department has a team of data analysts and a team of data engineers that use analytics systems.
The data engineers perform the ingestion, transformation, and loading of data. They prefer to use Python or SQL to transform the data.
The data analysts query data and create semantic models and reports. They are qualified to write queries in Power Query and T-SQL.
Existing Environment. Fabric
Contoso has an F64 capacity named Cap1. All Fabric users are allowed to create items.
Contoso has two workspaces named WorkspaceA and WorkspaceB that currently use Pro license mode.
Existing Environment. Source Systems
Contoso has a point of sale (POS) system named POS1 that uses an instance of SQL Server on Azure Virtual Machines in the same Microsoft Entra tenant as Fabric. The host virtual machine is on a private virtual network that has public access blocked. POS1 contains all the sales transactions that were processed on the company's website.
The company has a software as a service (SaaS) online marketing app named MAR1. MAR1 has seven entities. The entities contain data that relates to email open rates and interaction rates, as well as website interactions. The data can be exported from MAR1 by calling REST APIs. Each entity has a different endpoint.
Contoso has been using MAR1 for one year. Data from prior years is stored in Parquet files in an Amazon Simple Storage Service (Amazon S3) bucket. There are 12 files that range in size from 300 MB to 900 MB and relate to email interactions.
Existing Environment. Product Data
POS1 contains a product list and related data. The data comes from the following three tables:
In the data, products are related to product subcategories, and subcategories are related to product categories.
Existing Environment. Azure
Contoso has a Microsoft Entra tenant that has the following mail-enabled security groups:
Contoso has an Azure subscription.
The company has an existing Azure DevOps organization and creates a new project for repositories that relate to Fabric.
Existing Environment. User Problems
The VP of marketing at Contoso requires analysis on the effectiveness of different types of email content. It typically takes a week to manually compile and analyze the data. Contoso wants to reduce the time to less than one day by using Fabric.
The data engineering team has successfully exported data from MAR1. The team experiences transient connectivity errors, which causes the data exports to fail.
Requirements. Planned Changes
Contoso plans to create the following two lakehouses:
Additional items will be added to facilitate data ingestion and transformation.
Contoso plans to use Azure Repos for source control in Fabric.
Requirements. Technical Requirements
The new lakehouses must follow a medallion architecture by using the following three layers: bronze, silver, and gold. There will be extensive data cleansing required to populate the MAR1 data in the silver layer, including deduplication, the handling of missing values, and the standardizing of capitalization.
Each layer must be fully populated before moving on to the next layer. If any step in populating the lakehouses fails, an email must be sent to the data engineers.
Data imports must run simultaneously, when possible.
The use of email data from the Amazon S3 bucket must meet the following requirements:
Items that relate to data ingestion must meet the following requirements:
Lakehouses, data pipelines, and notebooks must be stored in WorkspaceA. Semantic models, reports, and dataflows must be stored in WorkspaceB.
Once a week, old files that are no longer referenced by a Delta table log must be removed.
Requirements. Data Transformation
In the POS1 product data, ProductID values are unique. The product dimension in the gold layer must include only active products from product list. Active products are identified by an IsActive value of 1.
Some product categories and subcategories are NOT assigned to any product. They are NOT analytically relevant and must be omitted from the product dimension in the gold layer.
Requirements. Data Security
Security in Fabric must meet the following requirements:
NEW QUESTION # 110
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 # 111
HOTSPOT
You have a Fabric workspace that contains a warehouse named DW1. DW1 contains the following tables and columns.
You need to create an output that presents the summarized values of all the order quantities by year and product. The results must include a summary of the order quantities at the year level for all the products.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 112
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: B
NEW QUESTION # 113
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