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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Implementing Data Engineering Solutions Using Microsoft Fabric |
| Exam Number: | DP-700 |
| Certificate Validity Period: | 12 Months (Renewable annually) |
| Real Exam Qty: | 50 - 60 |
| Available Languages: | Japanese, English, French, German, Portuguese (Brazil), Spanish, Simplified Chinese |
| Exam Price: | $165 USD |
| Exam Format: | Drag-and-drop, Case studies, Multiple choice, Scenario-based |
| Related Certifications: | Microsoft Certified: Fabric Data Engineer Associate |
| Passing Score: | 700 / 1000 |
| Exam Duration: | 100 minutes |
| 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 # 119
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?
Answer: B
Explanation:
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:
NEW QUESTION # 120
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?
Answer:
Explanation:
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.
NEW QUESTION # 121
You have a Fabric workspace that contains a large table named Table1. Table1 contains 2 billion rows.
You have a data source that generates a data file every 30 minutes The file contains only changes that occurred since the last file was generated.
You plan to deploy a data pipeline that will process each data file when it is generated and load the contents into Table1. You need to recommend which loading pattern to use for the following operations:
* Create new records.
* Delete existing records.
The solution must support the versioning of existing records.
What should you recommend for each operation? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 122
You have a Fabric .
In Warehouse1, you create a table named DimCustomer by running the following statement.
You need to set the Customerkey column as a primary key of the DimCustomer table.
Which three code segments should you run in sequence? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
NEW QUESTION # 123
You need to ensure that users can access updated data and build and view reports interactively. The solution must minimize the load on DB1.
What should you do?
Answer: A
Explanation:
The correct answer is C . Azure SQL Database mirroring continuously replicates supported source tables into OneLake as Delta tables , providing a near-real-time analytics-ready copy. Power BI can then use those mirrored Delta tables without repeatedly querying the operational Azure SQL database. Microsoft explicitly confirms that Power BI reports over mirrored Azure SQL data support Direct Lake because the mirrored tables are stored as V-Ordered Delta tables. Microsoft Learn Direct Lake is optimized for interactive analytics over Delta tables in OneLake. Instead of importing another physical copy into a Power BI semantic model or issuing every report query back to DB1, Direct Lake loads required columns directly from OneLake into the VertiPaq engine. This provides interactive report performance while keeping analytical query pressure off the operational database. Microsoft Learn Option B is inferior because DirectQuery sends queries to the underlying data source or SQL endpoint during report interaction and therefore introduces additional query latency and potential workload pressure. Options A and D introduce another extraction/import layer and require refresh operations, unnecessarily duplicating data that is already replicated into OneLake.
Study Guide reference: Implement and manage an analytics solution # OneLake and Mirroring # Direct Lake semantic models # optimize analytical access to operational data.
NEW QUESTION # 124
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