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Last Updated: Oct 23, 2026
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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Implementing Data Engineering Solutions Using Microsoft Fabric |
| Exam Number: | DP-700 |
| Related Certifications: | Microsoft Certified: Azure Data Engineer Associate Microsoft Certified: Fabric Analytics Engineer Associate |
| Exam Format: | Case studies, Scenario-based questions, Multiple response, Multiple choice |
| Exam Duration: | 100-120 |
| Certificate Validity Period: | 1 year (renewable) |
| Exam Price: | Varies by region (approx. $165 USD) |
| Passing Score: | 700/1000 |
| Real Exam Qty: | 40-60 |
| Available Languages: | Simplified Chinese, Japanese, Spanish, Portuguese (Brazil), Korean, French, English, German |
| Recommended Training: | Microsoft Fabric Documentation Microsoft Learn DP-700 Learning Path |
| Exam Registration: | Microsoft Learn Certification Exam Page Pearson VUE Microsoft Exams |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored via Pearson VUE or in-person at authorized testing centers |
| Pre Condition: | No formal prerequisites, but recommended experience with Azure data services and basic data engineering concepts |
| Official Syllabus URL: | https://learn.microsoft.com/credentials/certifications/exams/dp-700/ |
| Section | Objectives |
|---|---|
| Topic 1: Develop data transformation solutions | - Data modeling and lakehouse transformation
|
| Topic 2: Manage and monitor data solutions | - Monitor pipelines and workloads
|
| Topic 3: Secure and govern data in Microsoft Fabric | - Data governance and compliance
|
| Topic 4: Design and implement data ingestion solutions | - Integrate external data sources
|
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.
Correct Answer: C,E 🗳️
You have an Azure subscription that contains a blob storage account named sa1. Sa1 contains two files named Filelxsv and File2.csv.
You have a Fabric tenant that contains the items shown in the following table.
You need to configure Pipeline1 to perform the following actions:
* At 2 PM each day, process Filel.csv and load the file into flhl.
* At 5 PM each day. process File2.csv and load the file into flhl.
The solution must minimize development effort. What should you use?
Correct Answer: D 🗳️
You have a Fabric workspace that contains a warehouse named Warehouse1.
You have an on-premises Microsoft SQL Server database named Database1 that is accessed by using an on- premises data gateway.
You need to copy data from Database1 to Warehouse1.
Which item should you use?
Correct Answer: B 🗳️
You have a Fabric workspace named WS1 that contains several scheduled data pipelines. WS1 has workspace monitoring enabled, and the monitoring eventhouse contains a table named ItemJobEventLogs.
Users report that some downstream reports are missing newly ingested data.
You need to identify failed pipeline runs from the last nine minutes and return only the columns needed for troubleshooting.
How should you complete the KQL query?
Correct Answer:

Explanation:
First selection: JobType == ' Pipeline '
Second selection: SecondsAgo < = 540
Completed KQL query:
ItemJobEventLogs
| where JobType == ' Pipeline ' and JobStatus == ' Failed '
| extend SecondsAgo = datetime_diff( ' second ' , now(), ingestion_time())
| where SecondsAgo < = 540
| order by Timestamp desc
| project Timestamp, ItemName, WorkspaceName, JobStartTime, JobEndTime, JobStatus The correct selections are JobType == ' Pipeline ' and SecondsAgo < = 540 . Microsoft Fabric workspace monitoring writes item-level execution information to the ItemJobEventLogs table in the monitoring Eventhouse. Microsoft provides this specific KQL pattern for detecting recent pipeline failures , using JobType == ' Pipeline ' together with JobStatus == ' Failed ' .
The query then calculates:
datetime_diff( ' second ' , now(), ingestion_time())
This produces the approximate number of seconds elapsed since each monitoring record was ingested.
Because nine minutes = 540 seconds , the predicate SecondsAgo < = 540 retains records ingested during the preceding nine-minute window. SecondsAgo > = 540 would do the opposite by retaining records at least nine minutes old.
JobEndTime > = ago(9m) is not the correct selection because the query explicitly derives ingestion recency through ingestion_time(). Microsoft's documented workspace-monitoring example uses the same SecondsAgo < = 540 pattern.
Finally, project limits the result to the operational fields required for troubleshooting: timestamp, pipeline name, workspace, start/end times, and failure status.
Study Guide reference: Monitor and optimize an analytics solution # workspace monitoring # Eventhouse monitoring logs # KQL analysis of pipeline execution and failures.
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.
Correct Answer:

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.
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