Pass Guaranteed 2025 Pass-Sure Microsoft DP-700 Test Engine
Pass Guaranteed 2025 Pass-Sure Microsoft DP-700 Test Engine
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Microsoft Implementing Data Engineering Solutions Using Microsoft Fabric Sample Questions (Q59-Q64):
NEW QUESTION # 59
You need to schedule the population of the medallion layers to meet the technical requirements.
What should you do?
- A. Schedule multiple data pipelines.
- B. Schedule a data pipeline that calls other data pipelines.
- C. Schedule an Apache Spark job.
- D. Schedule a notebook.
Answer: B
Explanation:
The technical requirements specify that:
Why Use a Data Pipeline That Calls Other Data Pipelines?
- Sequential execution of child pipelines.
- Error handling to send email notifications upon failures.
- Parallel execution of tasks where possible (e.g., simultaneous imports into the bronze layer).
NEW QUESTION # 60
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. No
- B. Yes
Answer: A
Explanation:
Adding an aggregation like make_list() would require additional processing and memory, which could make the query slower.
NEW QUESTION # 61
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. CACHE
- C. VACUUM
- D. OPTIMIZE
Answer: D
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 # 62
Your company has a sales department that uses two Fabric workspaces named Workspace1 and Workspace2.
The company decides to implement a domain strategy to organize the workspaces.
You need to ensure that a user can perform the following tasks:
Create a new domain for the sales department.
Create two subdomains: one for the east region and one for the west region.
Assign Workspace1 to the east region subdomain.
Assign Workspace2 to the west region subdomain.
The solution must follow the principle of least privilege.
Which role should you assign to the user?
- A. Fabric admin
- B. domain contributor
- C. domain admin
- D. workspace Admin
Answer: C
Explanation:
To implement a domain strategy and manage subdomains within Fabric, the domain admin role is the appropriate role for the user. A domain admin has the permissions necessary to:
Create a new domain (for the sales department).
Create subdomains (for the east and west regions).
Assign workspaces (such as Workspace1 and Workspace2) to the appropriate subdomains.
The domain admin role allows for managing the structure and organization of workspaces in the context of domains and subdomains while maintaining the principle of least privilege by limiting the user's access to managing the domain structure specifically.
NEW QUESTION # 63
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
Answer: A
Explanation:
An outer join will include unmatched rows from both tables, increasing the dataset size and processing time. It does not improve query performance.
NEW QUESTION # 64
......
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