さらに、Pass4Test DP-700ダンプの一部が現在無料で提供されています：https://drive.google.com/open?id=1J0OT4lRYPSfHQbMsSKDfuTqW-wegLxci
私たちが提供するImplementing Data Engineering Solutions Using Microsoft Fabric準備トレントは、精巧にコンパイルされ、非常に効率的です。 DP-700試験トレントを練習するのに20〜30時間しかかからず、Pass4Test試験に参加できます。 仕事などで忙しいほとんどのお客様。 ただし、DP-700テスト準備を使用する場合、短時間で試験を準備して試験内容をマスターするのにMicrosoftそれほど時間は必要ありません。 彼らがする必要があるのは、毎日学習して練習するのに1〜2時間を費やし、DP-700テスト準備で簡単に試験に合格することです。 試験に合格するための時間と労力はほとんどかかりません。
| Section | Weight | Objectives |
|---|---|---|
| Ingest and transform data | 30-35% | - Ingest data using Data Factory pipelines and Dataflows Gen2 - Optimize and manage Delta tables - Transform data using Dataflows Gen2 and notebooks - Ingest and transform data using eventstreams - Design and implement loading patterns |
| Monitor and optimize an analytics solution | 30-35% | - Monitor Fabric items - Optimize performance of data storage and queries - Implement error handling in pipelines |
| Implement and manage an analytics solution | 30-35% | - Orchestrate processes with Data Factory pipelines - Implement security and governance - Configure and manage workspace settings and item access - Implement lifecycle management and source control for analytics assets |
Pass4TestのMicrosoftのDP-700試験トレーニング資料は豊富な経験を持っているIT専門家が研究したもので、問題と解答が緊密に結んでいるものです。それと比べるものがありません。専門的な団体と正確性の高いMicrosoftのDP-700問題集があるこそ、Pass4Testのサイトは世界的でDP-700試験トレーニングによっての試験合格率が一番高いです。Pass4Testを選んび、成功を選びます。
質問 # 84
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?
正解：F
解説：
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.
質問 # 85
You have a Fabric F32 capacity that contains a workspace. The workspace contains a warehouse named DW1 that is modelled by using MD5 hash surrogate keys.
DW1 contains a single fact table that has grown from 200 million rows to 500 million rows during the past year.
You have Microsoft Power BI reports that are based on Direct Lake. The reports show year-over-year values.
Users report that the performance of some of the reports has degraded over time and some visuals show errors.
You need to resolve the performance issues. The solution must meet the following requirements:
Provide the best query performance.
Minimize operational costs.
Which should you do?
正解：D
解説：
In this case, the key issue causing performance degradation likely stems from the use of MD5 hash surrogate keys. MD5 hashes are 128-bit values, which can be inefficient for large datasets like the 500 million rows in your fact table. Using a more efficient data type for surrogate keys (such as integer or bigint) would reduce the storage and processing overhead, leading to better query performance. This approach will improve performance while minimizing operational costs because it reduces the complexity of querying and indexing, as smaller data types are generally faster and more efficient to process.
質問 # 86
HOTSPOT
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.
正解：
解説：
Explanation:
質問 # 87
You have two Fabric notebooks named Load_Salesperson and Load_Orders that read data from Parquet files in a lakehouse. Load_Salesperson writes to a Delta table named dim_salesperson. Load.Orders writes to a Delta table named fact_orders and is dependent on the successful execution of Load_Salesperson.
You need to implement a pattern to dynamically execute Load_Salesperson and Load_Orders in the appropriate order by using a notebook.
How should you complete the code? To answer, drag the appropriate values the correct targets. Each value 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:
Topic 2, Contoso, LtdCase Study
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:
Products
ProductCategories
ProductSubcategories
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:
DataAnalysts: Contains the data analysts
DataEngineers: Contains the data engineers
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:
Lakehouse1: Will store both raw and cleansed data from the sources
Lakehouse2: Will serve data in a dimensional model to users for analytical queries 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:
Minimize egress costs associated with cross-cloud data access.
Prevent saving a copy of the raw data in the lakehouses.
Items that relate to data ingestion must meet the following requirements:
The items must be source controlled alongside other workspace items.
Ingested data must land in the bronze layer of Lakehouse1 in the Delta format.
No changes other than changes to the file formats must be implemented before the data lands in the bronze layer.
Development effort must be minimized and a built-in connection must be used to import the source data.
In the event of a connectivity error, the ingestion processes must attempt the connection again.
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:
The data engineers must have read and write access to all the lakehouses, including the underlying files.
The data analysts must only have read access to the Delta tables in the gold layer.
The data analysts must NOT have access to the data in the bronze and silver layers.
The data engineers must be able to commit changes to source control in WorkspaceA.
質問 # 88
You have an Azure SQL database named DBl
In a Fabric workspace, you deploy an eventstream named EventSlreamDBI to stream record changes from DB1 into a lakehouse.
You discover that events are NOT being propagated to EventStreamDBL You need to ensure that the events are propagated to EventStreamDBl. What should you do?
正解：B
質問 # 89
......
世界は変化している、我々はできるだけそのペースを維持する必要があります。我々Pass4TestはMicrosoftのDP-700試験の変化を注目しています。数年以来の試験問題集を研究しています。現在あなたに提供するのは大切なMicrosoftのDP-700資料です。あなたの購入してから、我々はあなたにMicrosoftのDP-700資料の更新情況をつど提供します。このサービスは無料なのです。あなたが我々の資料を購入するとき、あなたのMicrosoftのDP-700試験に関するすべてのヘルプを購入しました。
DP-700受験対策解説集: https://www.pass4test.jp/DP-700.html
ちなみに、Pass4Test DP-700の一部をクラウドストレージからダウンロードできます：https://drive.google.com/open?id=1J0OT4lRYPSfHQbMsSKDfuTqW-wegLxci