Practice Free DP-700 Exam Online Questions
You have a Fabric deployment pipeline that uses three workspaces named Dev, Test, and Prod.
You need to deploy an eventhouse as part of the deployment process.
What should you use to add the eventhouse to the deployment process?
- A . GitHub Actions
- B . a deployment pipeline
- C . an Azure DevOps pipeline
HOTSPOT
You have a Fabric workspace that contains two lakehouses named Lakehouse1 and Lakehouse2. Lakehouse1 contains staging data in a Delta table named Orderlines. Lakehouse2 contains a Type 2 slowly changing dimension (SCD) dimension table named Dim_Customer.
You need to build a query that will combine data from Orderlines and Dim_Customer to create a new fact table named Fact_Orders.
The new table must meet the following requirements:
– Enable the analysis of customer orders based on historical attributes.
– Enable the analysis of customer orders based on the current attributes.
How should you complete the statement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
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 move the filter to line 02.
Does this meet the goal?
- A . Yes
- B . No
A
Explanation:
Moving the filter to line 02: Filtering the Stream table before performing the join operation reduces the number of rows that need to be processed during the join. This is an effective optimization technique for queries involving large datasets.
You need to ensure that usage of the data in the Amazon S3 bucket meets the technical requirements.
What should you do?
- A . Create a workspace identity and enable high concurrency for the notebooks.
- B . Create a shortcut and ensure that caching is disabled for the workspace.
- C . Create a workspace identity and use the identity in a data pipeline.
- D . Create a shortcut and ensure that caching is enabled for the workspace.
B
Explanation:
To ensure that the usage of the data in the Amazon S3 bucket meets the technical requirements, we must address two key points:
Minimize egress costs associated with cross-cloud data access: Using a shortcut ensures that Fabric does not replicate the data from the S3 bucket into the lakehouse but rather provides direct access to the data in its original location. This minimizes cross-cloud data transfer and avoids additional egress costs.
Prevent saving a copy of the raw data in the lakehouses: Disabling caching ensures that the raw data is not copied or persisted in the Fabric workspace. The data is accessed on-demand directly from the Amazon S3 bucket.
You have a Fabric warehouse named DW1. DW1 contains a table that stores sales data and is used by multiple sales representatives.
You plan to implement row-level security (RLS).
You need to ensure that the sales representatives can see only their respective data.
Which warehouse object do you require to implement RLS?
- A . ISTORED PROCEDURE
- B . CONSTRAINT
- C . SCHEMA
- D . FUNCTION
D
Explanation:
To implement Row-Level Security (RLS) in a Fabric warehouse, you need to use a function that defines the security logic for filtering the rows of data based on the user’s identity or role. This function can be used in conjunction with a security policy to control access to specific rows in a table.
In the case of sales representatives, the function would define the filtering criteria (e.g., based on a column such as SalesRepID or SalesRepName), ensuring that each representative can only see their respective data.
HOTSPOT
You have a Fabric workspace named Workspace1_DEV that contains the following items:
– 10 reports
– Four notebooks
– Three lakehouses
– Two data pipelines
– Two Dataflow Gen1 dataflows
– Three Dataflow Gen2 dataflows
– Five semantic models that each has a scheduled refresh policy
You create a deployment pipeline named Pipeline1 to move items from Workspace1_DEV to a new workspace named Workspace1_TEST.
You deploy all the items from Workspace1_DEV to Workspace1_TEST.
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
HOTSPOT
You have a Fabric workspace that contains a lakehouse named Lakehousel.
Lakehousel contains a table named Status_Target that has the following columns:
• Key
• Status
• LastModified
The data source contains a table named Status.Source that has the same columns as Status_Target.
Status.Source is used to populate Status_Target. In a notebook name Notebook!, you load Status_Source to a DataFrame named sourceDF and Status_Target to a DataFrame named targetDF.
You need to implement an incremental loading pattern by using Notebook-!. The solution must meet
the following requirements:
• For all the matching records that have the same value of key, update the value of LastModified in Status_Target to the value of LastModified in Status_Source.
• Insert all the records that exist in Status_Source that do NOT exist in Status_Target.
• Set the value of Status in Status_Target to inactive for all the records that were last modified more than seven days ago and that do NOT exist in Status.Source.
How should you complete the statement? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
What should you do to optimize the query experience for the business users?
- A . Enable V-Order.
- B . Create and update statistics.
- C . Run the VACUUM command.
- D . Introduce primary keys.
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?
- A . Yes
- B . no
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:
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 200 million rows to 500 million 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?
- A . Change the MD5 hash to SHA256.
- B . Increase the capacity.C Enable V-Order
- C . Modify the surrogate keys to use a different data type.
- D . Create views.
C
Explanation:
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.