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Microsoft
Exam Questions DP-700
Implementing Data Engineering Solutions Using Microsoft Fabric (beta)

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NEW QUESTION 1
- (Topic 1)
You need to populate the MAR1 data in the bronze layer.
Which two types of activities should you include in the pipeline? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.

A. ForEach
B. Copy data
C. WebHook
D. Stored procedure

Answer: AB

Explanation:
MAR1 has seven entities, each accessible via a different API endpoint. A ForEach activity is required to iterate over these endpoints to fetch data from each one. It
enables dynamic execution of API calls for each entity.
The Copy data activity is the primary mechanism to extract data from REST APIs and load it into the bronze layer in Delta format. It supports native connectors for
REST APIs and Delta, minimizing development effort.
You need to schedule the population of the medallion layers to meet the technical requirements.
What should you do?
* A. Schedule a data pipeline that calls other data pipelines.
* B. Schedule a notebook.
* C. Schedule an Apache Spark job.
* D. Schedule multiple data pipelines.
* Answer: A
The technical requirements specify that:
Medallion layers must be fully populated sequentially (bronze silver gold). Each layer must be populated before the next.
If any step fails, the process must notify the data engineers. Data imports should run simultaneously when possible.
Why Use a Data Pipeline That Calls Other Data Pipelines?
A data pipeline provides a modular and reusable approach to orchestrating the sequential population of medallion layers.
By calling other pipelines, each pipeline can focus on populating a specific layer (bronze, silver, or gold), simplifying development and maintenance.
A parent pipeline can handle:
- 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 2
- (Topic 1)
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.

Answer: 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.

NEW QUESTION 3
DRAG DROP - (Topic 2)
You need to ensure that the authors can see only their respective sales data.
How should you complete the statement? To answer, drag the appropriate values the correct targets. Each value may be used once, more than once, or not at all.
You may need to drag the split bar between panes or scroll to view content
NOTE: Each correct selection is worth one point.

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A. Mastered
B. Not Mastered

Answer: A

Explanation:

NEW QUESTION 4
- (Topic 2)
You need to implement the solution for the book reviews.
Which should you do?

A. Create a Dataflow Gen2 dataflow.

B. Create a shortcut.
C. Enable external data sharing.
D. Create a data pipeline.

Answer: B

Explanation:
The requirement specifies that Litware plans to make the book reviews available in the lakehouse without making a copy of the data. In this case, creating a
shortcut in Fabric is the most appropriate solution. A shortcut is a reference to the external data, and it allows Litware to access the book reviews stored in Amazon
S3 without duplicating the data into the lakehouse.

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NEW QUESTION 5
HOTSPOT - (Topic 2)
You need to troubleshoot the ad-hoc query issue.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

A. Mastered
B. Not Mastered

Answer: A

Explanation:
SELECT last_run_start_time, last_run_command: These fields will help identify the execution details of the long-running queries.
FROM queryinsights.long_running_queries: The correct solution is to check the long- running queries using the queryinsights.long_running_queries view, which
provides insights into queries that take longer than expected to execute.
WHERE last_run_total_elapsed_time_ms > 7200000: This condition filters queries that took more than 2 hours to complete (7200000 milliseconds), which is
relevant to the issue described.
AND number_of_failed_runs > 1: This condition is key for identifying queries that have failed more than once, helping to isolate the problematic queries that cause
failures and need attention.

NEW QUESTION 6
- (Topic 2)
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.

Answer: B

NEW QUESTION 7
- (Topic 3)
You have a Fabric workspace that contains a warehouse named Warehouse1.
While monitoring Warehouse1, you discover that query performance has degraded during the last 60 minutes.
You need to isolate all the queries that were run during the last 60 minutes. The results must include the username of the users that submitted the queries and the

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query statements. What should you use?

A. the Microsoft Fabric Capacity Metrics app

B. views from the queryinsights schema
C. Query activity
D. the sys.dm_exec_requests dynamic management view

Answer: B

NEW QUESTION 8
- (Topic 3)
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 output JSON.
B. From Monitoring hub, select the latest failed run of Pipeline1, and then view the input JSON.
C. From Real-time hub, select Fabric events, and then review the details of Microsoft.Fabric.ItemReadFailed.
D. From Real-time hub, select Fabric events, and then review the details of Microsof
E. Fabric.ItemUpdateFailed.

Answer: B

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 9
- (Topic 3)
You have a Fabric workspace that contains a lakehouse named Lakehouse1.
In an external data source, you have data files that are 500 GB each. A new file is added every day.
You need to ingest the data into Lakehouse1 without applying any transformations. The solution must meet the following requirements
Trigger the process when a new file is added.
Provide the highest throughput.
Which type of item should you use to ingest the data?

A. Event stream
B. Dataflow Gen2
C. Streaming dataset
D. Data pipeline

Answer: A

Explanation:
To ingest large files (500 GB each) from an external data source into Lakehouse1 with high throughput and to trigger the process when a new file is added, an
Eventstream is the best solution.
An Eventstream in Fabric is designed for handling real-time data streams and can efficiently ingest large files as soon as they are added to an external source. It is
optimized for high throughput and can be configured to trigger upon detecting new files, allowing for fast and continuous ingestion of data with minimal delay.

NEW QUESTION 10
HOTSPOT - (Topic 3)
You have a Fabric workspace that contains a warehouse named Warehouse1. Warehouse! contains a table named Customer. Customer contains the following
data.

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You have an internal Microsoft Entra user named User1 that has an email address of user1@contoso.com.
You need to provide User1 with access to the Customer table. The solution must prevent User1 from accessing the CreditCard column.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

A. Mastered
B. Not Mastered

Answer: A

Explanation:

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NEW QUESTION 10
- (Topic 3)
You have a Fabric workspace that contains a Real-Time Intelligence solution and an eventhouse.
Users report that from OneLake file explorer, they cannot see the data from the eventhouse.
You enable OneLake availability for the eventhouse. What will be copied to OneLake?

A. only data added to new databases that are added to the eventhouse
B. only the existing data in the eventhouse
C. no data
D. both new data and existing data in the eventhouse
E. only new data added to the eventhouse

Answer: D

Explanation:
When you enable OneLake availability for an eventhouse, both new and existing data in the eventhouse will be copied to OneLake. This feature ensures that data,
whether newly ingested or already present, becomes available for access through OneLake, making it easier for users to interact with and explore the data directly
from OneLake file explorer.

NEW QUESTION 11
- (Topic 3)
You have a Fabric workspace named Workspace1 that contains a notebook named Notebook1.
In Workspace1, you create a new notebook named Notebook2.
You need to ensure that you can attach Notebook2 to the same Apache Spark session as Notebook1.
What should you do?

A. Enable high concurrency for notebooks.

B. Enable dynamic allocation for the Spark pool.
C. Change the runtime version.
D. Increase the number of executors.

Answer: A

Explanation:
To ensure that Notebook2 can attach to the same Apache Spark session as Notebook1, you need to enable high concurrency for notebooks. High concurrency
allows multiple notebooks to share a Spark session, enabling them to run within the same Spark context and thus share resources like cached data, session state,
and compute capabilities. This is particularly useful when you need notebooks to run in sequence or together while leveraging shared resources.

NEW QUESTION 15
HOTSPOT - (Topic 3)
You have a Fabric workspace named Workspace1 that contains a warehouse named Warehouse2. A team of data analysts has Viewer role access to
Workspace1. You create a table by running the following statement.

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You need to ensure that the team can view only the first two characters and the last four characters of the Creditcard attribute.
How should you complete the statement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

A. Mastered
B. Not Mastered

Answer: A

Explanation:

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NEW QUESTION 18
- (Topic 3)
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.

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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. Yes
B. No

Answer: B

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 19
- (Topic 3)
You need to develop an orchestration solution in fabric that will load each item one after the other. The solution must be scheduled to run every 15 minutes. Which
type of item should you use?

A. warehouse
B. data pipeline
C. Dataflow Gen2 dataflow
D. notebook

Answer: B

NEW QUESTION 21
DRAG DROP - (Topic 3)
You are building a data loading pattern by using a Fabric data pipeline. The source is an Azure SQL database that contains 25 tables. The destination is a
lakehouse.
In a warehouse, you create a control table named Control.Object as shown in the exhibit. (Click the Exhibit tab.)
You need to build a data pipeline that will support the dynamic ingestion of the tables listed in the control table by using a single execution.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the
correct order.

A. Mastered
B. Not Mastered

Answer: A

Explanation:

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NEW QUESTION 22
- (Topic 3)
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

Answer: B

Explanation:
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:

NEW QUESTION 27
HOTSPOT - (Topic 3)
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.

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A. Mastered
B. Not Mastered

Answer: A

Explanation:

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NEW QUESTION 28
- (Topic 3)
You have a Fabric workspace that contains a warehouse named Warehouse1.
You have an on-premises Microsoft SQL Server database named Database1 that is accessed by using an on-premises data gateway.
You need to copy data from Database1 to Warehouse1. Which item should you use?

A. a Dataflow Gen1 dataflow

B. a data pipeline
C. a KQL queryset
D. a notebook

Answer: B

Explanation:
To copy data from an on-premises Microsoft SQL Server database (Database1) to a warehouse (Warehouse1) in Microsoft Fabric, the best option is to use a data
pipeline. A data pipeline in Fabric allows for the orchestration of data movement, from source to destination, using connectors, transformations, and scheduled
workflows. Since the data is being transferred from an on-premises database and requires the use of a data gateway, a data pipeline provides the appropriate
framework to facilitate this data movement efficiently and reliably.

NEW QUESTION 32
HOTSPOT - (Topic 3)
You need to recommend a Fabric streaming solution that will use the sources shown in the following table.

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The solution must minimize development effort.

What should you include in the recommendation for each source? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

A. Mastered
B. Not Mastered

Answer: A

Explanation:

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NEW QUESTION 33
- (Topic 3)
You have an Azure key vault named KeyVaultl that contains secrets.
You have a Fabric workspace named Workspace-!. Workspace! contains a notebook named Notebookl that performs the following tasks:
• Loads stage data to the target tables in a lakehouse
• Triggers the refresh of a semantic model
You plan to add functionality to Notebookl that will use the Fabric API to monitor the semantic model refreshes. You need to retrieve the registered application ID
and secret from KeyVaultl to generate the authentication token.
Solution: You use the following code segment:
Use notebookutils.credentials.getSecret and specify the key vault URL and key vault secret. Does this meet the goal?

A. Yes
B. No

Answer: A

NEW QUESTION 36
- (Topic 3)
You have five Fabric workspaces.
You are monitoring the execution of items by using Monitoring hub.
You need to identify in which workspace a specific item runs. Which column should you view in Monitoring hub?

A. Start time
B. Capacity
C. Activity name
D. Submitter
E. Item type
F. Job type
G. Location

Answer: G

Explanation:
To identify in which workspace a specific item runs in Monitoring hub, you should view the Location column. This column indicates the workspace where the item is
executed. Since you have multiple workspaces and need to track the execution of items across them, the Location column will show you the exact workspace
associated with each item or job execution.

NEW QUESTION 41
- (Topic 3)
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?

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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.

Answer: D

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.

NEW QUESTION 46
HOTSPOT - (Topic 3)
You are building a data loading pattern for Fabric notebook workloads. You have the following code segment:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

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A. Mastered
B. Not Mastered

Answer: A

Explanation:

NEW QUESTION 50
- (Topic 3)
You have a Fabric workspace named Workspace1. Your company acquires GitHub licenses.
You need to configure source control for Workpace1 to use GitHub. The solution must follow the principle of least privilege. Which permissions do you require to
ensure that you can commit code to GitHub?

A. Actions (Read and write) and Contents (Read and write)

B. Actions (Read and write) only
C. Contents (Read and write) only
D. Contents (Read) and Commit statuses (Read and write)

Answer: C

NEW QUESTION 52
- (Topic 3)
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.

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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 55
- (Topic 3)
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 1 MB each. You need to minimize how long it takes to query Table1.
What should you do?

A. Disable V-Order and run the OPTIMIZE command.

B. Disable V-Order and run the VACUUM command.
C. Run the OPTIMIZE and VACUUM commands.

Answer: C

Explanation:
Problem Overview:
Table1 has 2,000 small Parquet files (1 MB each).
Query performance suffers when the table contains numerous small files because the query engine must process each file individually, leading to significant
overhead.
Solution:
To improve performance, file compaction is necessary to reduce the number of small files and create larger, optimized files.
Commands and Their Roles: OPTIMIZE Command:
- Compacts small Parquet files into larger files to improve query performance.
- It supports optional features like V-Order, which organizes data for efficient scanning. VACUUM Command:
- 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.

NEW QUESTION 60

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DRAG DROP - (Topic 3)

Your company has a team of developers. The team creates Python libraries of reusable code that is used to transform data.
You create a Fabric workspace name Workspace1 that will be used to develop extract, transform, and load (ETL) solutions by using notebooks.
You need to ensure that the libraries are available by default to new notebooks in Workspace1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the
correct order.

A. Mastered
B. Not Mastered

Answer: A

Explanation:

NEW QUESTION 64
- (Topic 3)
You have a Fabric workspace named Workspace1 that contains a data pipeline named Pipeline1 and a lakehouse named Lakehouse1.
You have a deployment pipeline named deployPipeline1 that deploys Workspace1 to Workspace2.
You restructure Workspace1 by adding a folder named Folder1 and moving Pipeline1 to Folder1.
You use deployPipeline1 to deploy Workspace1 to Workspace2. What occurs to Workspace2?

A. Folder1 is created, Pipeline1 moves to Folder1, and Lakehouse1 is deployed.

B. Only Pipeline1 and Lakehouse1 are deployed.
C. Folder1 is created, and Pipeline1 and Lakehouse1 move to Folder1.
D. Only Folder1 is created and Pipeline1 moves to Folder1.

Answer: A

Explanation:
When you restructure Workspace1 by adding a new folder (Folder1) and moving Pipeline1 into it, deployPipeline1 will deploy the entire structure of Workspace1 to
Workspace2, preserving the changes made in Workspace1. This includes:

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Folder1 will be created in Workspace2, mirroring the structure in Workspace1.

Pipeline1 will be moved into Folder1 in Workspace2, maintaining the same folder structure. Lakehouse1 will be deployed to Workspace2 as it exists in
Workspace1.

NEW QUESTION 69
- (Topic 3)
You have a Fabric workspace named Workspace1 that contains an Apache Spark job definition named Job1.
You have an Azure SQL database named Source1 that has public internet access disabled.
You need to ensure that Job1 can access the data in Source1. What should you create?

A. an on-premises data gateway

B. a managed private endpoint
C. an integration runtime
D. a data management gateway

Answer: B

Explanation:
To allow Job1 in Workspace1 to access an Azure SQL database (Source1) with public internet access disabled, you need to create a managed private endpoint. A
managed private endpoint is a secure, private connection that enables services like Fabric (or other Azure services) to access resources such as databases,
storage accounts, or other services within a virtual network (VNet) without requiring public internet access. This approach maintains the security and integrity of
your data while enabling access to the Azure SQL database.

NEW QUESTION 71
HOTSPOT - (Topic 3)
You are processing streaming data from an external data provider. You have the following code segment.

For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.

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A. Mastered
B. Not Mastered

Answer: A

Explanation:
Litware from New York will be displayed at the top of the result set – Yes
The data is sorted first by Location in descending order and then by UnitsSold in descending order. Since "New York" is alphabetically the last Location, it will
appear first in the result set. Within "New York", Litware has the highest UnitsSold (1000), so it will be displayed at the top.
Fabrikam in Seattle will have value = 2 in the Rank column – No
The row_rank_dense function assigns dense ranks based on UnitsSold within each location. In "Seattle":
Contoso has UnitsSold = 300 Rank 1 Litware has UnitsSold = 100 Rank 2
Fabrikam also has UnitsSold = 100, so it shares the same rank (2) as Litware.
Litware in San Francisco will have the same value in the Rank column as Litware in New York – No
The rank is calculated separately for each location. In "San Francisco":
Both Relecloud and Litware have UnitsSold = 500, so they share the same rank (1). In "New York", Litware has the highest UnitsSold = 1000 Rank 1.
Since ranks are calculated independently for each location, Litware in San Francisco does not share the same rank as Litware in New York.

NEW QUESTION 74
- (Topic 3)
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.

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Does this meet the goal?

A. Yes
B. No

Answer: 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.

NEW QUESTION 77
......

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https://www.surepassexam.com/DP-700-exam-dumps.html (98 New Questions)

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