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| Certification Vendor: | Microsoft |
|---|---|
| Exam Name: | Implementing Data Engineering Solutions Using Microsoft Fabric |
| Exam Number: | DP-700 |
| Available Languages: | Portuguese (Brazil), Spanish, German, Japanese, Simplified Chinese, English, French, Korean |
| Certificate Validity Period: | 1 year (renewable) |
| Passing Score: | 700/1000 |
| Exam Format: | Case studies, Scenario-based questions, Multiple response, Multiple choice |
| Related Certifications: | Microsoft Certified: Azure Data Engineer Associate Microsoft Certified: Fabric Analytics Engineer Associate |
| Exam Price: | Varies by region (approx. $165 USD) |
| Real Exam Qty: | 40-60 |
| Exam Duration: | 100-120 |
| Recommended Training: | Microsoft Learn DP-700 Learning Path Microsoft Fabric Documentation |
| Exam Registration: | Microsoft Learn Certification Exam Page Pearson VUE Microsoft Exams |
| Sample Questions: | Microsoft DP-700 Sample Questions |
| 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/ |
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NEW QUESTION # 54
You need to ensure that the data engineers are notified if any step in populating the lakehouses fails. The solution must meet the technical requirements and minimize development effort.
What should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
NEW QUESTION # 55
You have a Fabric workspace that contains an eventhouse and a KQL database named Database1. Database1 has the following:
A table named Table1
A table named Table2
An update policy named Policy1
Policy1 sends data from Table1 to Table2.
The following is a sample of the data in Table2.
Recently, the following actions were performed on Table1:
An additional element named temperature was added to the StreamData column.
The data type of the Timestamp column was changed to date.
The data type of the DeviceId column was changed to string.
You plan to load additional records to Table2.
Which two records will load from Table1 to Table2? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
Answer: A,D
Explanation:
Changes to Table1 Structure:
StreamData column: An additional temperature element was added.
Timestamp column: Data type changed from datetime to date.
DeviceId column: Data type changed from guid to string.
Impact of Changes:
Only records that comply with Table2's structure will load.
Records that deviate from Table2's column data types or structure will be rejected.
Record B:
Timestamp: Matches Table2 (datetime format).
DeviceId: Matches Table2 (guid format).
StreamData: Contains only the index and eventid, which matches Table2.
Accepted because it fully matches Table2's structure and data types.
Record D:
Timestamp: Matches Table2 (datetime format).
DeviceId: Matches Table2 (guid format).
StreamData: Matches Table2's structure.
Accepted because it fully matches Table2's structure and data types.
NEW QUESTION # 56
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?
Answer: D
Explanation:
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.
NEW QUESTION # 57
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: A
NEW QUESTION # 58
You have a Fabric workspace that contains a lakehouse and a notebook named Notebook1. Notebook1 reads data into a DataFrame from a table named Table1 and applies transformation logic. The data from the DataFrame is then written to a new Delta table named Table2 by using a merge operation.
You need to consolidate the underlying Parquet files in Table1.
Which command should you run?
Answer: C
Explanation:
To consolidate the underlying Parquet files in Table1 and improve query performance by optimizing the data layout, you should use the OPTIMIZE command in Delta Lake. The OPTIMIZE command coalesces smaller files into larger ones and reorganizes the data for more efficient reads. This is particularly useful when working with large datasets in Delta tables, as it helps reduce the number of files and improves performance for subsequent queries or operations like MERGE.
NEW QUESTION # 59
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