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DP-700オンライン試験 & DP-700学習関連題

Xhs1991はMicrosoftのDP-700「Implementing Data Engineering Solutions Using Microsoft Fabric」試験に向けて問題集を提供する専門できなサイトで、君の専門知識を向上させるだけでなく、一回に試験に合格するのを目標にして、君がいい仕事がさがせるのを一生懸命頑張ったウェブサイトでございます。

常々、時間とお金ばかり効果がないです。正しい方法は大切です。我々Xhs1991は一番効果的な方法を探してあなたにMicrosoftのDP-700試験に合格させます。弊社のMicrosoftのDP-700ソフトを購入するのを決めるとき、我々は各方面であなたに保障を提供します。購入した前の無料の試み、購入するときのお支払いへの保障、購入した一年間の無料更新MicrosoftのDP-700試験に失敗した全額での返金…これらは我々のお客様への承諾です。

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効果的なDP-700オンライン試験 & 合格スムーズDP-700学習関連題 | 一番優秀なDP-700日本語試験対策 Implementing Data Engineering Solutions Using Microsoft Fabric

Xhs1991は最高のハイパスレートDP-700トレーニング資料を提供しており、数千人の受験者が試験をクリアして夢のような認定を得るのに役立ちます。認定が傑出しているか重要であるほど、競争は激しくなります。 DP-700の実践教材は、あなたが簡単に目立つようにするあなたの勝利の魔法です。 DP-700学習ガイドには、効率的な準備に役立つ実際のテストに関する最も重要な知識が含まれています。 100％の合格率を追求する場合、DP-700試験の質問と回答は、わずか20〜30時間の学習で確実にクリアするのに役立ちます。

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

• 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 2

• 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.

トピック 3

• 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.

Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric 認定 DP-700 試験問題 (Q78-Q83):

質問 # 78
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

正解：A

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

質問 # 79
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?

• A. no
• B. Yes

正解：A

解説：
This code does not meet the goal because this is an SQL-like query and cannot be executed in KQL, which is required for the database.
Correct code should look like:

質問 # 80
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:
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?

• A. no
• B. Yes

正解：B

解説：
Filter Condition: It correctly filters rows where Neighbourhood is "Sands End" and No_Bikes is greater than or equal to 15.
Sorting: The sorting is explicitly done by No_Bikes in ascending order using sort by No_Bikes asc.
Projection: It projects the required columns (BikepointID, Street, Neighbourhood, No_Bikes, No_Empty_Docks, Timestamp), which minimizes the data returned for consumption.

質問 # 81
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：

質問 # 82
You have a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Table1.
You analyze Table1 and discover that Table1 contains 2,000 Parquet files of 1 MB each.
You need to minimize how long it takes to query Table1.
What should you do?

• A. Disable V-Order and run the VACUUM command.
• B. Run the OPTIMIZE and VACUUM commands.
• C. Disable V-Order and run the OPTIMIZE command.

正解：B

解説：
Problem Overview:
Table1 has 2,000 small Parquet files (1 MB each).
Query performance suffers when the table contains numerous small files because the query engine must process each file individually, leading to significant overhead.
Solution:
To improve performance, file compaction is necessary to reduce the number of small files and create larger, optimized files.
Commands and Their Roles:
OPTIMIZE Command:
- Compacts small Parquet files into larger files to improve query performance.
- It supports optional features like V-Order, which organizes data for efficient scanning.
VACUUM Command:
- Removes old, unreferenced data files and metadata from the Delta table.
- Running VACUUM after OPTIMIZE ensures unnecessary files are cleaned up, reducing storage overhead and improving performance.

質問 # 83
......

DP-700学習教材の練習試験や模擬試験はみんなにとって重要です。今のリビジョン条件はDP-700試験に参加する良い機会です。したがって、レビュープランを調整するために、DP-700の各練習問題を要約することが不可欠です。今、私たちはDP-700実際試験を模擬するためにオンラインテストエンジンとWindowsソフトウェアを追加しました。

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