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You need to ensure that usage of the data in the Amazon S3 bucket meets the technical requirements.
What should you do?
To ensure that the usage of the data in the Amazon S3 bucket meets the technical requirements, we must address two key points:
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?
This code does not meet the goal because it uses sort by without specifying the order, which defaults to ascending, but explicitly mentioning asc improves clarity.
Correct code should look like:
You have a Fabric workspace named Workspace1 that contains a warehouse named DW1 and a data pipeline named Pipeline1.
You plan to add a user named User3 to Workspace1.
You need to ensure that User3 can perform the following actions:
View all the items in Workspace1.
Update the tables in DW1.
The solution must follow the principle of least privilege.
You already assigned the appropriate object-level permissions to DW1.
Which workspace role should you assign to User3?
To ensure User3 can view all items in Workspace1 and update the tables in DW1, the most appropriate workspace role to assign is the Contributor role. This role allows User3 to:
View all items in Workspace1: The Contributor role provides the ability to view all objects within the workspace, such as data pipelines, warehouses, and other resources.
Update the tables in DW1: The Contributor role allows User3 to modify or update resources within the workspace, including the tables in DW1, assuming that appropriate object-level permissions are set for the warehouse.
This role adheres to the principle of least privilege, as it provides the necessary permissions without granting broader administrative rights.
You have a Fabric F32 capacity that contains a workspace. The workspace contains a warehouse named DW1 that is modelled by using MD5 hash surrogate keys.
DW1 contains a single fact table that has grown from 200million rows to 500million rows during the past year.
You have Microsoft Power BI reports that are based on Direct Lake. The reports show year-over-year values.
Users report that the performance of some of the reports has degraded over time and some visuals show errors.
You need to resolve the performance issues. The solution must meet the following requirements:
Provide the best query performance.
Minimize operational costs.
Which should you do?
In this case, the key issue causing performance degradation likely stems from the use of MD5 hash surrogate keys. MD5 hashes are 128-bit values, which can be inefficient for large datasets like the 500 million rows in your fact table. Using a more efficient data type for surrogate keys (such as integer or bigint) would reduce the storage and processing overhead, leading to better query performance. This approach will improve performance while minimizing operational costs because it reduces the complexity of querying and indexing, as smaller data types are generally faster and more efficient to process.
You have a Fabric workspace that contains a lakehouse named Lakehouse1. Lakehouse1 contains a Delta table named Table1.
You analyze Table1 and discover that Table1 contains 2,000 Parquet files of 1MB each.
You need to minimize how long it takes to query Table1.
What should you do?
Problem Overview:
Solution:
Commands and Their Roles:
- Compacts small Parquet files into larger files to improve query performance.
- It supports optional features like V-Order, which organizes data for efficient scanning.
- Removes old, unreferenced data files and metadata from the Delta table.
- Running VACUUM after OPTIMIZE ensures unnecessary files are cleaned up, reducing storage overhead and improving performance.
