MICROSOFT DP-700 PASS GUARANTEED & EXAM DP-700 FEES

Microsoft DP-700 Pass Guaranteed & Exam DP-700 Fees

Microsoft DP-700 Pass Guaranteed & Exam DP-700 Fees

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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q64-Q69):

NEW QUESTION # 64
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 add the make_list() function to the output columns.
Does this meet the goal?

  • A. Yes
  • B. No

Answer: B

Explanation:
Adding an aggregation like make_list() would require additional processing and memory, which could make the query slower.


NEW QUESTION # 65
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?

  • A. BROADCAST
  • B. OPTIMIZE
  • C. VACUUM
  • D. CACHE

Answer: B

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 # 66
You have a Fabric warehouse named DW1 that loads data by using a data pipeline named Pipeline1. Pipeline1 uses a Copy data activity with a dynamic SQL source. Pipeline1 is scheduled to run every 15 minutes.
You discover that Pipeline1 keeps failing.
You need to identify which SQL query was executed when the pipeline failed.
What should you do?

  • A. From Monitoring hub, select the latest failed run of Pipeline1, and then view the input JSON.
  • B. From Real-time hub, select Fabric events, and then review the details of Microsoft. Fabric.ItemUpdateFailed.
  • C. From Real-time hub, select Fabric events, and then review the details of Microsoft.Fabric.ItemReadFailed.
  • D. From Monitoring hub, select the latest failed run of Pipeline1, and then view the output JSON.

Answer: A

Explanation:
The input JSON contains the configuration details and parameters passed to the Copy data activity during execution, including the dynamically generated SQL query.
Viewing the input JSON for the failed pipeline run provides direct insight into what query was executed at the time of failure.


NEW QUESTION # 67
You have a Fabric workspace.
You are debugging a statement and discover the following issues:
You need to resolve the issues. The solution must ensure that the data types of the results are retained. The results can contain blank cells.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:

Explanation:


NEW QUESTION # 68
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. Yes
  • B. no

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 # 69
......

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