A retail company uses Real-Time Intelligence to analyze clickstream data. They have an existing KQL database that stores raw events. A team needs to run frequent interactive KQL queries that join the raw events with a large dimension table stored in OneLake as a shortcut. Query performance is currently poor. What should they do to improve performance without duplicating the dimension data?

Convert the shortcut to a native table in the KQL database.

Enable query acceleration on the OneLake shortcut for the dimension table.

Move the dimension table into the KQL database as an external table.

Create a materialized view that includes the dimension data.

A weather monitoring company streams sensor data into a KQL database. They need to calculate the average temperature per sensor per 5-minute window, but sensors may send data with a delay of up to 2 minutes. The team needs to handle late-arriving data accurately and prefers to use KQL for all processing. What should you do?

Use an Eventstream transformation to group events into 5-minute windows and include a 2-minute delay.

Use Spark Structured Streaming with a 5-minute tumbling window and a 2-minute watermark.

Use KQL's `sliding_window_counts` with a 5-minute window and a 2-minute offset.

Use KQL's `summarize` with a 5-minute `bin` on the event time, and configure the query to include a 2-minute lookback period to capture late events.

A logistics company has a OneLake shortcut in their KQL database pointing to a delta table that receives frequent updates from a Spark streaming job. Analysts run KQL queries that aggregate the last hour of shipment data and need results within seconds. The current query performance is slow. What should you do to improve query performance without duplicating the data?

Use a standard OneLake shortcut and rely on KQL's caching.

Convert the shortcut to a native table in the KQL database.

Enable query acceleration on the OneLake shortcut.

Move the Spark streaming job to write directly to the KQL database.

Microsoft Certified:Fabric Data Engineer Associate

Domain 2Objective 3

Ingest and Transform Streaming Data DP-700 Practice Questions (Page 1)

Part of the Ingest and transform data domain, which accounts for 30–35% of the DP-700 exam. Microsoft does not publish an official question count, but from its 100-minute exam (~40–65 total, ~12–23 in this domain), expect 4–8 from this objective — we provide 25 practice questions to prepare you well beyond it. (estimate)

25questions here

5free pages

7concepts

30–35%of the exam

Questions 1–5

1application · medium
A retail company uses Real-Time Intelligence to analyze clickstream data. They have an existing KQL database that stores raw events. A team needs to run frequent interactive KQL queries that join the raw events with a large dimension table stored in OneLake as a shortcut. Query performance is currently poor. What should they do to improve performance without duplicating the dimension data?
Select an answer first
2expert · hard
A weather monitoring company streams sensor data into a KQL database. They need to calculate the average temperature per sensor per 5-minute window, but sensors may send data with a delay of up to 2 minutes. The team needs to handle late-arriving data accurately and prefers to use KQL for all processing. What should you do?
Select an answer first
3foundation · easy
What is the primary abstraction used in Spark Structured Streaming to represent a stream of data?
Select an answer first
4application · medium
A logistics company has a OneLake shortcut in their KQL database pointing to a delta table that receives frequent updates from a Spark streaming job. Analysts run KQL queries that aggregate the last hour of shipment data and need results within seconds. The current query performance is slow. What should you do to improve query performance without duplicating the data?
Select an answer first
5foundation · easy
In KQL, which operator is used to aggregate data from a streaming source, such as calculating an average?
Select an answer first

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Ingest and Transform Streaming Data DP-700 Practice Questions (Page 1)

Questions 1–5

What is the primary abstraction used in Spark Structured Streaming to represent a stream of data?

In KQL, which operator is used to aggregate data from a streaming source, such as calculating an average?

Keep studying — Ingest and transform data